Sven Wohlgemuth
email: sven.wohlgemuth@trust.cased.de

Adaptive User-Centered Security

Keywords: Security, privacy, usability, resilience, identity management 1 Data-Centric Society and Security

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concern [START_REF]Vertrauen und Sicherheit im Internet. DIVSI Milieu Study on Trust and Security on the Internet -Condensed version[END_REF]. The key issue to be resolved is usage of (personal) data in compliant to agreed-upon social and business rules. This is necessary to achieve acceptable quantity and quality of required information [START_REF] Otto | Information and data quality in business networking: a key concept for enterprises in its early stages of development[END_REF], which reduces error rate of data-centric services and so a vulnerability by misuse of (personal) information.

Privacy by Design and User-Centered Security

Beside citizens as participants in an information exchange service providers of datacentric services become attractive for cyber-attacks [START_REF]The IT Security Situation in Germany in[END_REF]. Incidents arise mainly via third parties [START_REF] De Keukelaere | Adaptive Security Dialogs for Improved Security Behaviors of Users[END_REF], i.e. dependencies between IT systems participating in an information exchange. Privacy by Design postulates to consider IT security requirements in all phases of software development to reduce vulnerabilities [START_REF] Anderson | Security Engineering: A Guide to Building Dependable Distributed Systems 2nd edition[END_REF][START_REF] Eckert | IT-Sicherheit: Konzepte, Verfahren[END_REF]. Software engineering process models are enriched by threat and risk modeling, which combine functional requirements as liveness properties with IT security requirements as safety properties [START_REF] Alpern | Defining Liveness[END_REF]. An equilibrium of the participant's individual security interests [START_REF] Rannenberg | IT Security and Multilateral Security[END_REF] specifies a privacy policy, which formalizes 'balanced' safety and liveness requirements as security properties [START_REF] Clarkson | Hyperproperties[END_REF]. Isolation separates then trustworthy from nontrustworthy participants as well as reliable IT systems from failed ones. Irrespective of a software development process, the scope of implementing Privacy by Design ends at present after the release of an IT system. Its enforcement of a privacy policy holds as long as events and executions of the IT system during run-time correspond to its security model. Data-centric services, however, constantly changes their dependencies due to information exchanges with other users.

User-Centered Security extends Privacy by Design by integrating users' requirements and view on the IT system of an information exchange into the threat model and IT security architecture [START_REF] Zurko | User-Centered Security: Stepping Up to the Grand Challenge[END_REF]. Even though an iterative software development process model with short cycles might reduce the consequences of a security incident, it reacts on a vulnerability instead of preventing their exploitation. In addition, enforceability of a privacy policy, and in general of a security policy, is at present decided by rigorous enforceability of safety properties. Security mechanisms are statistical program analysis, signaling with equivalent security policies as detectors, monitoring control traces with enforcement monitors, and re-writing control traces. The result is that enforcement of safety properties can violate required liveness properties and it is not decidable in case of vulnerabilities by non-observable traces [START_REF] Hamlen | Computability Classes for Enforcement Mechanisms[END_REF], e.g. covert channels. This is as well the challenge for enforcing the 'right to be forgotten' as granted to European citizens as a countermeasure against misuse of personal information [START_REF]Court of Justice of the European Union. Judgment of the Court (Grand Chamber) of 13 May 2014 ([END_REF].

Contribution

The contribution of this work is Adaptive User-Centered Security in adapting the threat model, IT security model, and its enforcement to users as participants in an information exchange, dependencies, and incidents of an IT system during run-time.

In contrary to the rigorous aim of strictly enforcing safety properties, adaptive usercentric security aims at an acceptable enforcement of an equilibrium between safety and liveness requirements according to the risk tolerance of the given user. An adaptation component configures and enforces individual security interests on behalf of the user as far as desired and possible. Starting point is the electronic identity (eID) of a user as his electronic representation in the Internet.

Adaptation to the User

Security mechanisms need to be used and configured without loss of information according to the privacy policy of an information exchange. The basics are a user model supporting the target user groups, establishment of trust domains with specific safety and liveness requirements, and a measurement on authenticity of information of an exchange. An adaptive user interface should prevent privacy vulnerabilities due to an interaction with the given user as far as possible according to the user model and privacy policy of an information exchange. It considers user interactions for the security configuration of an IT system and scale its enforcement according to the privacy expectations of the user and properties of the security mechanisms. Depending on the results of a measurement, a change in the privacy policy for the isolation and the usage of security mechanisms should improve security and privacy and remain the information exchange acceptable or at least brittle, which means that an additional incident will turn the IT risk to be unacceptable for the affected user.

Security-relevant User Interactions

The user interface of security tools must fulfil two requirements. On the one hand, it should offer the user all the necessary information about the configuration of security mechanisms, and on the other hand the user should interact with it as few as possible for achieving the goal of his activity with an IT service. Usability studies for security tools [START_REF] Waidner | Open Issues in Secure Electronic Commerce[END_REF][START_REF] Whitten | Why Johnny can't encrypt: A Usability Evaluation of PGP 5.0. SSYM'99[END_REF] show that their current user interfaces threaten an enforcement of a policy, since the user interfaces are driven by IT security concepts. A user has to learn these technical concepts and adapt to it.

In order to configure all IT security protection goals, a user needs to explicitly configure accountability and unobservability by his eID. Due to dependencies of IT security protection goals, confidentiality can be controlled by the IT system. Integrity can be automatically controlled so that a user interaction needs only take place in case of a non-acceptable anomaly of integrity [START_REF] Jendricke | Usability Meets Securitythe Identity-Manager As Your Personal Security Assistant for the Internet[END_REF]. In addition to this configuration by safety requirements of a privacy policy, its liveness requirements define obligations on the availability of data according to the purpose of data processing, storage, their further disclosure and removal [START_REF] Karjoth | A Privacy Model for Enterprises[END_REF].

In case of accountability and unobservability, an IT security situation depends on the user's configuration, otherwise it is user-independent. If the current vulnerability is part of the system's threat model, the IT security situation is independent on a manual user's decision, otherwise dependent. If an information exchange depends on the context, the IT system can control enforcement of the privacy policy as long as the threat model considers the current security vulnerability under investigation. If the context has no dependency to the privacy policy, e.g. integrity can always be assured without raising an additional vulnerability, the situation is context-independent. These dependencies result in four classes of an IT security (Table 1).

Context-independent

Scalability for Enforcement of a Privacy Policy

Starting point for a user-centered enforcement of a privacy policy is identity management to achieve accountability with the digital representation of a user [START_REF] Saltzer | The Protection of Information in Computer Systems[END_REF]. Identity management systems according to Chaum [START_REF] Chaum | Security without Identification: Transaction Systems to make Big Brother Obsolete[END_REF] and with anonymous credentials [START_REF] Gerd Tom Markotten | Mit Sicherheit zukunftsfähig[END_REF] are suitable for the enforcement of an adaptable user-centered security model, since they support accountability and unobservability by authentication with pseudonyms without raising any vulnerability by contradicting with a liveness requirement of an information exchange. Even though identity management supports end-to-end security of an information exchange by authentication to an intermediary, dependencies between the IT systems of participants in a data-centric service imply at the same time a vulnerability of non-observable traces between these IT systems. A compromise of participants and their IT system can thus not be ruled out. Dependencies need to be considered for the user, threat, and IT security model as well as for enforcement.

Regarding enforcement, these vulnerabilities relate to the threat model of Dolev and Yao [START_REF] Dolev | On the Security of Public Key Protocols[END_REF], in which security is based on perfect security of cryptographic public key protocols, secure and available public directory of cryptographic public keys, and confidentiality of cryptographic secret keys. Since identity management ensures authenticity of identity but not of exchange information, e.g., such as the necessary cryptographic public key for cryptographic protection of an information exchange, either an authentic pre-key sharing or an authentic key exchange via a third party is required to enforce IT security. A third party as an intermediary in information exchanges is in particular threatened by hidden dependencies, as IT security analysis for IT systems of data-centric services show. The concluding approach is ICT Resilience, which takes dependencies and incidents of any kind for IT security into account [START_REF] Wohlgemuth | Security and Privacy in Business Networking[END_REF]. So that additional vulnerabilities don't arise when formalizing the IT security model and its policies for an information exchange, the privacy policy model for adaptive user-centered security in general is usage control. Usage control considers obligations and does not raise additional vulnerabilities by its concept.

Enforcement of privacy and security policies, respectively, differs according to their enforceability in static analysis of the IT system, enforcement by a monitor, and by re-writing [START_REF] Hamlen | Computability Classes for Enforcement Mechanisms[END_REF] whereas the approach differs in preventing, tolerating, removing, or forecasting vulnerabilities [START_REF] Avižienis | Basic Concepts and Taxonomy of Dependable and Secure Computing[END_REF]. According to different security interests of the participants, their self-protection requires Privacy-Enhancing Technologies (PET), Transparency-Enhancing Technologies (TET), or a mixed mode of their operation.

Scalability for IT security requires a semantically accurate mapping of the required safety and liveness properties to different user interfaces for configuring privacy policies and their enforcement by the IT security architecture with its different security mechanisms PETs and TETs. Thereby, scalability may not raise by itself a vulnerability across these IT security abstraction layers. Dependencies between these layers should be minimized so that changes within one layer does not affect internals of the other layers. The Model-View-Controller (MVC) software pattern for user interfaces [START_REF] Gamma | Design Patterns. Elements of Reusable Object-Oriented Software[END_REF] to enhance usability for non-experts, e.g., as deployed for film production processes [START_REF] Kajiyama | Evaluation of an Improved Visualization System for Helping Children Identify Risky Websites[END_REF], is suitable for Adaptive User-Centered Security. It considers dependencies only between abstraction layers but not across their internal state transitions.

System Evolution Cycle

The system evolution cycle aims at adapting an IT security system to changes, vulnerabilities, and incidents during run-time. Figure 1 shows the process of adaptation. User modeling gets input from two sources: From the initial evaluation and modelling by a user survey and from system tests. For the Internet usage in Germany, the study on trust and security on the Internet [START_REF]Vertrauen und Sicherheit im Internet. DIVSI Milieu Study on Trust and Security on the Internet -Condensed version[END_REF] is a starting point for the user modelling. System tests during run-time derive an evidence on anomalies of a policy violation on isolation and their information accountability [START_REF] Weitzner | Information Accountability[END_REF] in combination for using security mechanisms for unobservability. In order to detect evidence on anomalies in information, a system test predicts and re-constructs the provenance on this information to be derived or on derived information, respectively. The idea is to classify information and their provenance to patterns, since this kind of monitoring does not change the state of the observed IT system. Patterns represent categories of enforcement with isolation patterns and vulnerabilities as well as incidents by anti-isolation patterns with machine learning algorithms. This requires log data as observation by sensors on the provenance of the information under investigation. The adaptation component initializes this system evolution cycle and continuously re-configures the IT security architecture while evaluating evidences of system tests and user's interactions.

Adaptive System Model for IT Security

The MVC software pattern allows a scalable adaptation of the IT security architecture to the given user model, system model, and available security mechanisms. According to the current approach of Privacy by Design, the IT security architecture with its security mechanisms for controlling isolation follows immediately after the specification of the IT security model. The consequence is a direct dependency between the state transitions of the model with a security mechanisms for their enforcement. A change of such a dependency requires a change in the model or security architecture with a re-validation of its security. The adaptation component aims exactly at a continuous re-validation of isolation. This give a view to the user on his privacy during run-time. According to the MVC software pattern, the adaptation component is an abstraction layer between then concrete IT system architecture and state of the isolation and the adaptive user interface with its user and security model (Figure 2). This model introduces user-centered isolation for an information exchange across the layers and isolation patterns on combining suitable, available security mechanism according to different classes of isolation and its expected results. Isolation refers to an information exchange between a user and his trust relationships to the other participants and their IT systems according to a 'balanced' IT privacy policy of their interests. In this context, isolation of an information exchange is seen as a special sort of privacy, which refers to a controllable data processing for this information exchange [START_REF] Sonehara | Isolation in Cloud Computing and Privacy-Enhancing Technologies -Suitability of Privacy-Enhancing Technologies for Separating Data Usage in Business Processes[END_REF]. Measurement of accountability of information derives an evidence on enforcement of a given isolation.

A certified isolation pattern can then be used for an equivalent scenario of an information exchange as well as for an exchange with other users. This allows the adaptation component to re-configure the IT system configuration according to a given user. System-centered isolation patterns complements it. They formalize a deployment of security mechanisms to enforce an isolation with acceptable risk according to the current and possible future states of the given IT system. If a previously unknown requirement, vulnerability, or incident occurs, the adaptation component can simulate deployment of known isolation patterns on this new situation, improve them, or develop a new isolation pattern. This results in a new view on the required isolation.

Adaptive User-Centered View on Information Exchange

A view on isolation derives a statement on the likelihood for authenticity and accountability of information, i.e. anomalies in the specified isolation and accountability of the data processing. A view differs in the model of a user including his trust assumptions and knowledge about suitable isolation patterns as well as evidence on their enforcement. Concerning a data provider, the view results in a prediction of the expected isolation; concerning a data consumer, the view results in detecting whether isolation of received information has been enforced and it can be seen as authentic.

IRiS: User-Centered IT Risk Analytics

An evaluation library called IRiS (Information exchange Risk Screening) derives a statistical evidence on authenticity on the data processing and resulting information for a user-centered view on this IT system. IRiS extends data mining capability with two mechanisms to record information flows and to reconstruct events, which have led to a deviation from acceptable states. The third extension is to incorporate liveness concepts that allow a usage in planning mode, i.e. prediction on isolation of an information exchange. IRiS consists of an IT risk analyzer (IRiS Analytics) and a database (IRiS Knowledge Database). The adaption component queries the IRiS Analytics whether the current isolation is acceptable according to the corresponding user and his membership to a group of the user model. The IRiS Analytics derives a statistical statement on the current information exchange from user's known statements about the participants in this data processing and isolation patterns for the expected isola-tion. The IRiS Knowledge Database stores this knowledge of the user and extends it with discovered isolation patterns and anomalies during run-time.

The challenge for IRiS IT risk analyzer whether information and, in turn, its data processing can be seen as being authentic is the same as in a PKI in providing a statement on the authenticity of a cryptographic public key. Irrespectively on the organizational model of a PKI, the evaluation model of Maurer [START_REF] Maurer | Modeling a Public-Key Infrastructure[END_REF] derives with propositional logic a user-centered view. Trust assumptions of a user A in other participants' enforcement of certification of the PKI, here isolation of the information exchange, is taken into account. Statements of a view refer to the cryptographic key of another participant X (AutA,X) known to the user A and taken as being authentic as well as on his belief in X, that is expressed by a trust statement (TrustA,X,i).

Still, cryptographic key certificates and recommendations need to be considered to obtain the user's trust in this view on an information exchange. A cryptographic key certificate issued by a participant X in the role of a certification authority (CA) on enforcement of isolation and this, in turn, on the reliability of the IT system of another participant Y (CertX,Y). A recommendation expresses a belief i of a participant X in a participant Y (RecX,Y,i) in that Y enforces the certification policy and, hence, can be trusted, i.e. privacy policy for this information exchange. Figure 3 Exemplary view of a user Alice on an information exchange according to [START_REF] Maurer | Modeling a Public-Key Infrastructure[END_REF]].

The aim of IRiS Analytics is to derive the statement AutA,B, here AutAlice,Bob, by the following rules [START_REF] Maurer | Modeling a Public-Key Infrastructure[END_REF] from the view of A=Alice.

 X,Y:

AutA,X, TrustA,X,1, CertX,Y  AutA,Y (1)
 X,Y,i1: AutA,X, TrustA,x,i+1, RecX,Y,i  TrustA,Y,i

 X,Y,1≤k<i: TrustA,X,i  TrustA,X,k

(3)  X,Y,1≤k<i: RecA,X,i  RecA,X,k (4)
Concerning the example, Alice considers the information from Bob as authentic, since AutAlice,Bob can be derived by one of the data traces in the directed graph of the data traces for this information. Afterwards IRiS Analytics checks whether the received information matches with acceptable probability to a cluster of expected valid information or it validity in case this information is the cryptographic public key of Bob or a certified statement of a credential. In case of information, a data clustering scheme should assign this information to a cluster of expected results according to the privacy policy and isolation patterns known to Alice. In case of a cryptographic public key, its validity will be checked according to the privacy policy on its authorized usage, timeliness, and revocation to grant access on Alice's IT system [START_REF] Blaze | Distributed Trust Management[END_REF].

IRiS Analytics checks security vulnerabilities as dependencies of the given instance of the workflow 'Information exchange' between the participating IT systems as a Black Box [START_REF] Accorsi | Information leak detection in business process models: Theory, application, and tool support[END_REF] and, if necessary and possible, additionally their internal data traces with a White Box test scheme [START_REF] Enck | Taint-Droid: An Information Flow Tracking System for Real-Time Privacy Monitoring on Smartphones[END_REF]. The former evaluation results in evidenceINFOR-MATION; the evaluation of the workflow instance results in evidenceDATA TRACE. Their combination contributes together with the statement AutA,X on evidenceISOLATION [START_REF] Wohlgemuth | Resilience as a new Enforcement Model for IT Security based on Usage Control. 5th International Workshop on Data Usage Management[END_REF]. However, even though if a data trace between nodes or a node itself is faulty, a data trace of another information exchange might be acceptable correct. Taking several data traces for exchanging the same information into account results, in turn, in a consensus on AutA,X. The evaluation model of [START_REF] Maurer | Modeling a Public-Key Infrastructure[END_REF] considers dependencies and incomplete knowledge about enforcement, which is the case for data traces of hidden dependencies, by a confidence parameter derived from a probability distribution on the set of possible initial views of a user.

The IRiS Database stores this knowledge including user interactions and system configurations of instances of the workflow 'Information exchange'. Since the outcome and costs of reconstructing archived information with the configuration of the corresponding instance are difficult to estimate, an approach is to emulate their reconstruction in possible future data processing environments using software [START_REF] Rechert | Take care of your belongings todaysecuring accessibility to complex electronic business processes[END_REF]. The aim is to identify clearly defined and controllable preservation strategies, which should be integrated in current business processes. Detected and robust patterns from other trustworthy participants should extend this knowledge as well as the robust isolation patterns of this user should be publicly available and exchanged with others. This information should be exchanged via an already established isolated channel, e.g. a proven trustworthy intermediary of an eID infrastructure. Since information leakage might occur due to a hidden dependency, isolation patterns should be anonymized before their disclosure while remaining accountable to detect the cause of such an information leakage. An approach is a k-anonymization scheme, which tags anonymized information by the procedure of their anonymization [START_REF] Kieseberg | An algorithm for collusion-resistant anonymization and fingerprinting of sensitive microdata[END_REF]. This evaluation considers colluding data consumers, as given by an incident propagation, with the aim of non-authorized re-identification of the related identity to this information.

Retrieving and Re-Writing Data Traces

IRiS Analytics derives a view on an IT system and exchange of personal information, which is built with the IT systems of the participants in an information exchange and eventually needs to be modified or dissolved during run-time. If a non-acceptable evidence on an anomaly derived by a system test or a new requirement from the user model occurs, a re-writing of the workflow [START_REF] Wang | Satisfiability and Resiliency in Workflow Authorization Systems[END_REF], the IT system [START_REF] Hamlen | Computability Classes for Enforcement Mechanisms[END_REF], or both is required in advance or during an information exchange, respectively, to prevent more severe incidents. Retrieving and re-writing data traces may not raise an additional vulnerability in enforcing the user's security interests on accountability and unobservability. Since these mechanisms consider isolation as a kind of privacy, they are called Privacy Forensics and Privacy Control.

Privacy Forensics aims at detecting evidence on isolation by the most probable data provenance history and its classification to an anomaly pattern. Information is tagged with a label, which represents the data providing and data consuming parties together with the corresponding privacy policy for this isolation. The provenance of information, and in general data, d consists of the data provider's, data consumer's, and the user's identity as well as a pointer to the privacy policy. The privacy policy is indirectly part of a tag by a link to it. This, in turn, allows the user to modify the privacy policy and the IT system, if the purpose of the data's usage changes or a severe vulnerability and incidents occurs. The tag should stick to d, so that d * =(d, tag) can be disclosed further while assuring the integrity of the relationship within d * . If d * is disclosed further, the tag has to be updated by adding the new role of this now data providing participant and adding the identity of the new data consumer. The sequence of tags for the same personal information thus constitutes its data trace.

Tagging of data requires authentic information for testing and to reduce the statistical error rate of the applied machine learning scheme. So that the necessary (personal) data as log data don't violate the protection goal unobservability, each log data is encrypted and related log data for deriving an evidence on isolation of a given information exchange are linked by a chain of cryptographic hash values. Linked log data represent a log view on a data provider's information. Each log view is individualized to the identity of the corresponding data provider. Collection and retrieval log views depend on a trusted execution environment to which the data provider or an authorized participant, e.g. an auditor, authenticates with his identity to get access [START_REF] Accorsi | A secure log architecture to support remote auditing[END_REF]. Since internal traces of a sub IT system are not known, but labeled evidence exists by the specification of this data processing, supervised machine learning can be useful for deriving evidenceDATA TRACE. Since not all kind of data can be annotated, mechanisms of unsupervised machine learning should also be researched to establish their suitability. The derived data provenance has some safety and liveness properties, which are expressed by a policy as its detector [START_REF] Hamlen | Computability Classes for Enforcement Mechanisms[END_REF]. If this policy is not equivalent to the privacy policy of the expected isolation, an anomaly has been detected.

Privacy Control aims at re-writing the 'code' according to changes in the privacy policy and supporting at the same time self-protection against information leakage.

The main identity of users as data providers remains unobservable, when the eID infrastructure supports pseudonymity as well as non-linkable delegation and revocation of rights [START_REF] Sonehara | Isolation in Cloud Computing and Privacy-Enhancing Technologies -Suitability of Privacy-Enhancing Technologies for Separating Data Usage in Business Processes[END_REF]. Pseudonymity should be revocable should provable fraud have occurred. According to distributed trust management [START_REF] Blaze | Distributed Trust Management[END_REF], an orchestration according to authorizations can be done by delegation and revocation of rights with credentials. Credentials are a representation of access rights, which are delegated to service providers to obtain access to personal data in agreement with the individual in question. Anonymized credential schemes achieves unobservability in the issuing and showing protocols [START_REF] Camenisch | An Efficient System for Non-transferable Anonymous Credentials with Optional Anonymity Revocation[END_REF]. The individual (in the role of the data subject) specifies these access decisions by delegating the access rights together with obligations or using these access rights to the requesting service provider (in the role of a data consumer). To obtain a user's agreement for each disclosure of his personal data to a third party, it should be possible to delegate and revoke rights for isolating an information exchange. Thereby, a delegation of rights defines a collaboration between service providers including the exchange of given user's personal data between them.

Adaptive Identity Management System

Preliminary work of the author exists as partial identities for security-relevant user interactions, Privacy Control, and Privacy Forensics.

iManager: User Interactions and Personal IT Security Tool

iManager as an eID client for mobile use introduces partial identities for user interaction and a concept for automatic configuration of security mechanisms [START_REF] Wohlgemuth | DFG-Schwerpunktprogramm Sicherheit in der Informations-und Kommunikationstechnik[END_REF]. It offers interfaces to the user, security mechanisms, and applications of a mobile device. The access to personal data and to cryptographic keys is exclusively possible by using the identity manager. An application's request to these data will be checked by the identity manager to see whether the user has granted authorization to this access on personal data. Based on a security platform with the necessary security mechanisms in order to protect the communication, the personal data and the privacy of the user, the components identity configuration, identity negotiation, and confirmation of action are responsible for managing partial identities. The concept of partial identities for security-relevant user interactions and its implementation for the iManager has been developed according to the software development process model of User-Centered Security Engineering (UCSec) [START_REF] Gerd Tom Markotten | User-Centered Security Engineering[END_REF]. UCSec combines usability engineering for development of user interfaces with security engineering.

DREISAM: Non-Linkable Delegation of Rights for Privacy Control

DREISAM extends an eID infrastructure for an unobservable delegation and revocation of rights to third parties [START_REF] Sonehara | Isolation in Cloud Computing and Privacy-Enhancing Technologies -Suitability of Privacy-Enhancing Technologies for Separating Data Usage in Business Processes[END_REF]. The higher cryptographic protocols of DREISAM combine the mechanisms for delegation of rights by credentials with mechanisms for enforcing non-linkability for unobservability when using credentials. Anonymous credentials make use of a cryptographic commitment scheme for binding authorizations to a cryptographic key and of zero-knowledge proofs for showing this relationship without revealing any identifying data. Since a user would lose control on his identity, if he would use anonymous credentials for delegation, a proxy credential replaces sharing of individual's master identity. It represents to the certification authority (CA) the individual's delegation request for a certain right to a service provider. If the service provider gets a proxy credential, he has the individual's authorisation to get the requested access right by means of an anonymous credential. The CA logs requests from users and service providers with the issued proxy and anonymous credentials in the delegation list. The CA uses this list for checking service providers' requests for anonymous credentials and for resolving disputes between participants.

DETECTIVE: Data Provenance Protocols for Privacy Forensics

DETECTIVE is an experimental data provenance system with the aim of privacypreserving tracing disclosure of data to third parties even in case of covert channels [START_REF] Wohlgemuth | Tagging Disclosures of Personal Data to Third Parties to Preserve Privacy[END_REF]. DETECTIVE makes use of cryptographic commitments and of a symmetric digital watermarking algorithm but without the need of a trustworthy data provider or a TTP regarding the embedding and checking of data provenance information. Cryptographic commitments link the identities of the participating service providers in any disclosure of personal data. Digital watermarking is used to tag the corresponding personal data with this link. Since users do not take part in the disclosures of personal data, users give their authorization in advance, e.g. by using DREISAM. DETECTIVE operates in the three phases (1) definition of the collaborating service providers of a business process by obligations and their delegation according to the privacy policy, (2) documenting disclosures of personal data to third parties by adding data provenance as a digital watermark, and (3) checking the enforcement of the obligations by comparing the delegated rights with the data provenance information of the found personal data.

Related Work

Adaptation of IT security enforcement is considered for user interfaces and on getting access on data, but without proposing a concept for adaptation in general and in case of an information exchange. Recent work configures screen locking of a device for non-authorized access via the GUI according to the current physical environment of the (mobile) device. The physical environment represent the context on which a machine learning scheme derives a classification and classify the current context. The focus is on data disclosure, i.e. on data collection, of location data [START_REF] Miettinen | ConXsense -Context Profiling and Classification for Context-Aware Access Control[END_REF].

Adaptation of security-relevant user interactions on IT security considers the design of a GUI, which addresses different level of user risk [START_REF] De Keukelaere | Adaptive Security Dialogs for Improved Security Behaviors of Users[END_REF]. A dialogue adaptation engine tracks user's behavior, generates security dialogues, and provides feedback to the user. It uses collected security information to alter the behavior of the dialogs. Security information is stored in data stores, which is decision risk, user performance, and environmental data. Decision risk data are executions with high risks, user performance data refers whether a user differentiates between non-risk and risky operations, and environmental data are other external data. However, there is no assurance that this systems runs as expected, since it assumes an IT system without vulnerabilities. It has been shown that a misuse of GUI elements by introducing hidden GUI elements is possible and an exploit of this vulnerability results in information leakage.

Approaches for detecting security vulnerabilities by assessing a GUI consider certain classes of vulnerabilities and user group, but not a general user and threat model [START_REF] Kajiyama | Evaluation of an Improved Visualization System for Helping Children Identify Risky Websites[END_REF][START_REF] Mulliner | Hidden GEMs: Automated Discovery of Access Control Vulnerabilities in Graphical User Interfaces[END_REF].

Conclusion

Adapting an IT system of an information exchange to security vulnerabilities during run-time to acceptable enforce individual security interests would improve security and privacy by reducing security vulnerabilities in an isolation. However, this inherits a privacy paradoxon. Whereas PETs should impede a privacy violation by restricting availability personal information, they impede at the same time a detection of security vulnerabilities in isolation of an information exchange and, hence, threaten privacy as understood by isolation of an information exchange. On the one side, accountability of information with TETs, as demanded with transparent, accountable data flow tracking by the Big Data and Privacy 90-day review of The White House [START_REF]Big Data: Seizing Opportunities, Preserving Values[END_REF], requires authentic personal information to reduce the error rate of an adaptation. On the other side, usage of personal information according to an isolation, hence security and privacy, is threatened by hidden dependencies such as an exploit of a covert channel for an information leakage and modification of information. The proposal for Adaptive User-Centered Security should contribute to identify and enforce an equilibrium in such a multilateral settings between the individual security and privacy interests of participants in an information exchange, among others on security incidents.

Figure 1

 1 Figure 1 Procedure for continuous adaptation of IT security enforcement.

Figure 2 3 -

 3 Figure 2 3-Layer-Security-Model introducing abstraction for user-centered isolation.

Figure 3

 3 illustrates an exemplary view of a user X=Alice on the exchange of information from a user Y=Bob with the intermediaries and statements of user C=System 3 and D=System 4. The red arrow represents the requested statement whether Alice can consider the information from Bob as being authentic (AutAlice,Bob). The blue arrows represent the statements for the information exchange via System 3 on authenticity of data processing (AutAlice,System3), certification (CertSystem 3,Bob), and trust (TrustAlice, System 3,2). The black arrows represent statements known to Alice on an exchange of the same information via participant System 4: AutAlice,System 4, CertSystem 4,Bob, CertSystem 3,System 4, and TrustAlice,System 4,1.

Priva- cy Policy Context-dependent Privacy Policy User- independent Configuration Class 1: No

		user interaction	Class 2: Controlled by the IT
		necessary, totally controlled	security system, if situation can
		by the IT security system	be detected; otherwise user
			interaction necessary
	User-Configuration dependent	Class 3: User interaction nec-the IT security system tion, then totally controlled by essary for initial configura-	Class 4: User interaction neces-sary, controlled by the user

Table 1

 1 Classes of IT security situations.

Acknowledgement

Basics of this work was funded by the German Research Foundation (DFG) within the priority program 'Security in the Information and Communication Technology' (SPP 1079) under coordination of Günter Müller. I would like to thank all members of the corresponding project group 'ATUS -A Toolkit for Usable Security', Isao Echizen and Stefan Sackmann for the discussions on resilience and IT risk in social infrastructures, and the reviewers of CD-ARES 2014 for their valuable comments.


Sao-Khue Vo

Amin Anjomshoaa
email: anjomshoaa@ifs.tuwien.ac.at

Min Tjoa

Semantic-aware Mashups for Personal Resources in SemanticLIFE and SocialLIFE

Keywords: Semantic Desktop, Semantic Web, Social Networks, Linked Data, Linked Data Services, Semantic Mashup

SemanticLIFE is a Semantic Desktop system, which deals with the personal lifetime data. However, SemanticLIFE is limited to local storage, which is an isolated data repository. In the recent years, people have the tendency to share their resources, which are not only stored locally on their personal computers, but also hosted on social networking sites (SNSs). We propose and use the term 'SocialLIFE' to denote one's lifetime information in SNSs, in which personal resources are his/her activities, interests, and related connections. In this paper, we also propose a mashup language and a semantic-based mashup framework. The final goal of this research is to provide a semanticbased way for bridging the gap between SemanticLIFE and SocialLIFE in order to integrate and reuse existing personal resources of existing applications such as information resources of Semantic Desktops and SNSs. The proposed mashup system also aims to supports non-expert users to create data mashups based on semantic-aware mashup dataflow.

1

Background and Motivation

Business objectives are accomplished successfully when human resource management systems are developed and implemented according to organizational goals, particularly if personal information management (PIM) is adopted to utilize all employee information productively [START_REF] Baird | Managing Two Fits of Strategic Human Resource Management[END_REF]. Some PIM systems use Semantic Desktop approach, which create a semantic layer for integrating applications and personal life items, as the means to support users in information management. Today, an increasing number of organizations/enterprises are taking advantage of mashups, which support users in fast integration of heterogeneous data from multiple sources [START_REF] Berners-Lee | Linked Data[END_REF] [START_REF] Anjomshoaa | Combining and integrating advanced ITconcepts with semantic web technology mashups architecture case study[END_REF]. Furthermore, they also benefit from the collaborative principles of Web 2.0 technologies by using social networking sites (SNSs) to build their social activities/relations and support their knowledge management. As a result, the amount of heterogeneous data assets, which are distributed among personal and business domains, is increasing. In addition, those PIM systems are limited to isolated data repositories, and do not fulfill most of the requirements for a holistic collaborative environment at the organizational level. Although enterprise knowledge management systems facilitate reusing and accessing of knowledge resources, these systems cannot work effectively unless knowledge workers contribute their knowledge resources and assets to organizations/enterprises.

In order to support users in exploiting the potential of sharing data on the web, the data should be managed in a machine-processable way by applying Semantic Web technologies. During designing and discussing issues around the Semantic Web, Tim Berners-Lee came up with the new term "Linked Data" that describes a method for publishing and interlinking structured data so that it can become more useful for people or machines in exploring the Web of Data [START_REF] Bader | Proposing a context-aware enterprise mashup readiness assessment framework[END_REF]. In the meantime, we observe a number of contributions in the Enterprise 2.0 domain [START_REF] Hinchcliffe | 14 Reasons Why Enterprise 2.0 Projects Fail[END_REF] that are aiming to apply Web 2.0 and SNSs principles to create an effective and collaborative community, and to explore the role of social computing in achieving the performance goals of enterprises. The potential of Enterprise 2.0 cannot be fully realized without an active support and an increased involvement of human resources [START_REF] Metter | Enterprise 2.0 and HR -Realizing the Potential[END_REF]. Using SNSs in an enterprise environment, employees can share their data (e.g. skills, interests, or activities, etc.) with their groups or colleagues. From the enterprise perspective, employees can collect business information from customers and partners through SNSs by exploring the relationship of business establishments, professionals, or individuals.

From the above remarks, we can derive the need for a flexible and semantic-aware approach to bridge the gap between internal and external data sources, and especially bringing the relevant personal resources into enterprises. The ultimate goal of this paper is also to find the solution for semantic-aware mashups of personal resources from Semantic Desktops and SNSs following the trends of Enterprise 2.0.

Personal Resources in SemanticLIFE and SocialLIFE

SemanticLIFE is a prototype of a PIM system that has been developed in Vienna University of Technology to store, organize and manage various personal life items [START_REF] Ahmed | SemanticLIFE' -A Framework for Managing Information of A Human Lifetime[END_REF]. The architecture of SemanticLIFE framework is depicted in Fig. 1. It provides a repository of lifetime personal data from varied resources (e.g. email messages, web browser history, images, contacts, phone calls, life events, and other resources). However, the major limitation of Semantic Desktops approaches is that it is restricted to a personal computers and its precious semantic information is not yet effectively used in business processes and tasks that people deal with in their workplace and daily life [START_REF] Anjomshoaa | Integration of Personal Services into Global Business[END_REF].

With the advent of Web 2.0, many organizations/enterprises have started using the Web 2.0 techniques by applying SNSs in order to increase the effectiveness of their business by means of a tighter collaboration. People have the tendency to share their knowledge or resources, which are not only stored locally on their personal computer or isolated data repositories, but also hosted on SNSs on the web (e.g. Google documents, LinkedIn profiles, Flickr images, Twitter tweets, etc.).

Fig. 1. SemanticLIFE framework [6]

In our research, we propose and use the term 'SocialLIFE' to denote one's lifetime information in SNSs, in which personal resources are user activities (messages, comments, twits, etc.), user interests (books, movies, etc.), and other related connections (friends, colleagues, etc.). To be more specific, one's SocialLIFE consists of interconnections of people relations such as friendship or business/professional relationships on Facebook or LinkedIn; their interests such as video, image or music on YouTube, Flickr, MySpace and so on.

With the aim of interlinking data on the Semantic Web to support people or machines in exploring the Web of Data, Linked Data delivers an important mechanism for information management and information integration [START_REF] Berners-Lee | Linked Data[END_REF]. Linked Data extends the standard Web technologies such as HTTP, URIs, and RDF to share information in a machine-readable way. Besides, the term "Linked Open Data" (LOD), refers to a community project that is aiming to encourage people or organizations to follow the Linked Data principle and publish their raw data under open licenses [START_REF] Berners-Lee | Linked Data[END_REF]. The Semantic Web community has gained momentum with the widespread publishing of LOD and the big number of promising datasets with a high potential for further exploration. In order to bridge the gap between Semantic Desktops and Linked Data, several research projects are conducted [START_REF] Drăgan | Linking Semantic Desktop Data to the Web of Data[END_REF], [START_REF] Groza | Bridging the Gap between Linked Data and the Semantic Desktop[END_REF]. These researches have the common goals to combine resources from Linked Data and Semantic Desktops by using semantic metadata as a common denominator, and to enrich the data within the personal information as well as the enterprise spaces.

To benefit from the LOD cloud, it is crucial to put personal resources into a context that facilitates the interlinking of resources and enables powerful personal services. These personal resources are not just textual content but also multimedia content such as music, videos, pictures, etc. There are a number of well-known vocabularies such as FOAF, SIOC, DBPedia, etc., which empower data sharing and linking personal resources with LOD cloud. Furthermore, for the multimedia content, there are some application-specific exporters that allow users to export their data. For instance, Flickr's data can be exported via tools such as Flickr2RDF [START_REF] Kanzaki | Flickr photo info to RDF image description[END_REF] and FlickrWrappr [START_REF] Bizer | Flickr wrappr[END_REF] that parse the user profile and image metadata in order to generate the RDF serialization which reuses the FOAF vocabulary. With a high potential for further exploration, SNSs have served as convincing and useful platforms for linking and reusing heterogeneous data of a user's SocialLIFE for performing further operations or aggregations.

As a result, the existing ontology models and exporters can be reused to extend the SemanticLIFE ontology and support information integration with one's SocialLIFE. Figure 2, depicts the linking of personal resources in SemanticLIFE and SocialLIFE with the LOD cloud.

Fig. 2. Linking SemanticLIFE and SocialLIFE with LOD Cloud

To illustrate the potential of this approach, a motivating use case for personal financial data (such as bank statements) will be presented in Section 4.1. A number of financial institutions and banks allow their customers to access their financial statements via APIs or download their statements in standard formats such as Open Financial Exchange format. The Open Financial Exchange (OFX)1 is a unified specification to exchange electronic financial information between financial institutions and consumers via the Internet. In order to turn personal financial data into mashable artifacts, personal financial data can be converted to RDF/N3 and linked with personal profile.

All gathered information in Semantic Desktops and SNSs platforms have to be parsed into semantic personal services or mashable semantic-based resources that provide a semantic way to express and exchange information from heterogeneous resources. In this context, there are a number of research efforts that try to combine the services and Web of Data. Linked Data Services (LIDS) [START_REF] Bader | Privacy aspects of mashup architecture[END_REF] is one of these ap-proaches that by definition provides HTTP URIs for entities. Dereferencing the LIDS' URIs returns an RDF description of the service input entity, its relation to the service output and the output data itself. This facilitates the transition from data silos to the Web of Data and enables the automatic integration of data resources.

The LIDS is applied as a suitable solution in our approach, in which we use the vocabulary of LIDS to define and describe the relevant services. The advantage of the LIDS is its capability to build semantic models of Web APIs semi-automatically. This includes the lowering and lifting processes that is converting RDF to the required input data of services and constructing semantic data out of a non-semantic API or service response, respectively.

To illustrate this by an example, consider a sample service for searching personal events in SocialLIFE, which has the URL http://localhost/sociallife/thing. This service is also used in the latter mashup use cases, which will be presented in Section 0. In the lowering side of this service, the variables are added to the URL of this service as a query string, in which the variables of the corresponding SPARQL query "SELECT ?thing WHERE {?thing foaf:name ?q}" are bound to the variables of the service URL. For instance in case of querying the conference events, the SPARQL query will be rewritten as "?events foaf:name ?conference". In the lifting side, this service takes the name and the query variables to form the service URL "http://localhost/sociallife?thing=events&q=conference". If the client accepts RDF format, the service returns the results as RDF data. This example is illustrated more details in Figure 3. "?thing foaf:name ?q" Fig. 3. An example of semantic-enabled personal services based on LIDS 3

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Semantic-based Mashup

Mashup is a web application that uses content from different data sources to generate a new service presented in a single graphical interface2 . Based on the semantic approach, semantic mashup are mashups whose combined services and APIs are supported or annotated by a semantic layer for supporting (semi)-automatic service composition in mashup creation [START_REF] Speiser | Integrating Linked Data and Services with Linked Data Services[END_REF]. In this section, a semantic-based mashup architecture is proposed, which aims to: (i) facilitate the integration of heterogeneous personal resources from SemanticLIFE and SocialLIFE within organizations/enterprises; and (ii) improve the mashup usability by supporting the end users to choose the appropriate input data via mapping domain ontologies. This semantic-based mashup framework consists of the following four layers (as illustrated in Fig. 4): privacy policies for self-monitoring of shared information in social networks [START_REF] Malki | Building Semantic Mashup[END_REF]. Further details about this approach are provided in our previous article [START_REF] Anjomshoaa | Self-monitoring in social networks[END_REF]. ─ Mashup layer: In this layer, the mashable semantic data of lower layers will be integrated and used in various use cases of data integration. To create mashup, the services of SemanticLIFE and SocialLIFE are referred in the mashup language that will be introduced in Section 3.1. ─ Data visualization layer: This layer enhances the original contents by adding graphical representations like maps or images (e.g., Google Map, Flickr, etc.). This layer constitutes the main workspace that allows end-users to interact with the mashup platform. In our framework, the following three groups of services are supported: ─ SPARQL-based services: These services query the semantic data via dedicated SPARQL endpoints such as DBpedia3 , Events4 , etc. ─ Third party services: These services represent the third party APIs that do not expose RDF data. Examples of such APIs are Flickr, Google Map, etc. ─ Personal services: These services query the personal resources via custom personal services or data repository of Semantic Desktops. Our semantic-aware mashup platform is equipped with a resource mashup language, which is used to create widget compositions and dataflows based on the aforementioned services. In the following section, more details about this mashup language is provided.

Personal Resources Mashup Language

The proposed Personal Resources Mashup Language (PRML) aims to: ─ Create a simple mashup language, which uses the domain ontologies and helps developers to create semantic-aware data widgets. ─ Formulate the composite solutions based on connectable widgets in order to address the user requirements. The PRML has four main components, namely mashup, environment, widget, and parameter as described in Fig. 5.

The root element of PRML schema is the mashup element and contains an environment sub-element, which can be SemanticLIFE, SocialLIFE or any other predefined context. The third level of PRML contains the required widgets with the following primary attributes: ─ context and type: are the name and type of the required context respectively. ─ source: indicates the service source of widgets (SPARQL endpoint, third party service, or personal service). ─ role: indicates the role assigned to a specific user or a group of users who can use the widget.

─ mapping: supports mapping to ontology resources or properties, for example, map-ping="rdf:type :Place" is mapping the input/output with the Place concept of the target ontology. ─ parameters: indicates the additional parameters of the target widget (input and output parameters). The fourth level of PRML are parameters which contain one or more input and output elements with the following attributes: ─ type: is the type of input/output parameters (String, Number, Array, Boolean, Date). ─ mapping: supports mapping to the ontology resources or properties. ─ acceptedData/dataFormat: is the type of data format that is accepted by the input ports. The data format can be JSON, XML, SPARQL-results or object. ─ operator: identifies an operator to construct constraints (for instance: > , < , regular expression, etc.) ─ requireSource: indicates the input of the target widget if required.

Semantic Mashup Formalization

In this section, the formal definition of mashup concepts and mashup rules based the proposed mashup language will be defined.

Definition 1. Widget

A widget is a tuple of <I, R, P> where I = {i 1 ,i 2 ,… ,i p } is a set of Input ports, R is the result set and P is the process (or web service) which is run internally in the widget to consume the inputs I and produce the result R. Each one of these elements has a set of metadata that describes the functions and properties of that element. The input ports and output results will have the previously defined attributes of input and output parameter of PRML (e.g. type, mapping, acceptedData/dataFormat, etc.).

The process may include further metadata such as additional parameters for the widget (e.g. id, name, width, height…), process description, and type of process (e.g., SPARQL, Javascript based, Web Service, etc.). In order to use widgets, they should be instantiated and called by the mashup framework.

Definition 2. Mashup

A mashup M is a set of 4-tuple, M = {<w i ,c ij ,w j , I wj > | i,j=0…n, i ≠ j} where ─ w i , w j ∈ W are instances of widget (W is the set of available widgets) ─ c ij is the connector between two widgets that denotes the dataflow between output port of widget w i and the I wj input port of widget w j .

In order to help users to design mashups in a semantic way, some rules for the mashup process are needed that are defined as follows.

Let D = {d 1 , d 2 , ..., d q } be a set of data formats, and O = {o 1 , o 2 , ..., o r } be a set of ontology types.

Rule 1: Feasible Connection

A feasible connection between two widgets w i and w j is a connection c ij where the data format (d i ) or mapping ontology type (o i ) of the output port on widget w i side is compatible with d j or o j on the input port at widget w j side.

Rule 2: Avoiding loops

If the Mashup contains a 4-tuple <w i , c ii+1 , w i+1 , I wi+1 > then there should be no path <w i+1 , c i+1 i+2 , w i+2 , I wi+2 > ….<w i+n , c i+n i+n+1 , w i+n+1 , I wi+n+1 > where w i+n+1 = w i .

Widget-based Query generation

To query remote RDF resources, the corresponding query is constructed based on widget parameters as follows:

Suppose I = {i 1 , i 2 ,..., i n } is a set of input parameters of a given widget and R is the resulting output of this widget. Each input parameter i j (j=0…n) has also its optional properties (i.e., name, mapping, type, value and operator).

A query Q can be defined as: Q = <S, W, F> where: ─ S = <SELECT, R, I>: SELECT statement with relevant parameters ─ W = <WHERE, R, I>: WHERE clause with relevant parameters ─ F = <FILTER, I>: FILTER constrains with relevant required parameters Query Q is generated in three steps as described below (depicted in Fig. 6): ─ Step 1: The parameters' names are enumerated for creating the SELECT part. The SPARQL variables of SELECT statement are formed as "SELECT ?R ?i

Widget UI generation

In the proposed mashup system, UI Widgets are both a graphical user interface and a software component with a specific function. In PRML syntax, the UI Widgets can be described as a user interface with standard web form elements. In this section, a convenient and flexible UI generation mechanism for parsing and rendering the form widgets will be provided. This mechanism and the required steps for the UI Widget rendering process is described in the figure 7: The following example describes the generation steps of Widget UI with the appropriate ontologies and services from a PRML file (as depicted in Fig. 8): ─ The widget's parameters are extracted from the corresponding PRML file: for example, the BankStatements widget defines the following input parameters in the PRML description file: account, memo, amount, start_date, and end_date; and output parameter bank_statements. ─ Widget description files are then imported into the widget manager component. ─ Widget container validates and processes the PRML files. In this step, widget parameters are rendered as form elements that are also mapped to the appropriate concepts of domain ontologies. As such, each input parameter is represented as a form element (e.g. textbox, combo box or date field). For example, in case of BankStatements widget, the account input parameter will be represented as a textbox and is mapped to the ontology concept ofx:ACCTID and the input parameter start_date is rendered as a date field which is mapped to the ontology concept ofx:DTPOSTED, and so on. ─ The description of UI Widget will be also used to generate the required SPARQL query with relevant variables as described in section 3.3.

Defining

Mashup Algorithm

Suppose that a set of widgets {w i …w j } are used in a specific mashup composition M. If w j is requested to execute, then it is necessary to check whether wj requires output from another widget w i or not. If w j has no required input, then it can be executed independently. Otherwise, the requied widget that provides the required input needs to

Mapping domain ontology UI generation Define Bank Statements widget in PRML query generation

Fig. 8. An example of Widget UI and widget-based query generation be executed first. For this reason, a mashup algorithm for possible loop detection is required.

The algorithm for mashup execution is based on the acyclic directed graphs, in which each widget of mashup is considered as a graph vertex, and each connection between two widgets is considered as a graph edge. For iterating through all widgets of a mashup (nodes of the graph), the Depth first-search (DFS) algorithm is used to visit the widgets. In case of mashup, the output value of any selected widget will be the input value of the connected widgets via the relevant port. The mashup M and the visited widget will be handled by a recursive function. Each visited widget will be asked to execute its internal process and return the value via its output port. The returned value will be then considered as the input value for the connected widgets. Instead of checking all edges at once in DFS algorithm, the edges are checked in two phases by differentiating discovery edges and back edges via input ports or output port respectively. Discovery edges are those edges that connect a vertex to another descendant node, and back edges are those edges that connect a vertex to another ancestor node. The mashup execution algorithm is described in the following recursive pseudo code:

Use Cases

As a proof of concept of the proposed approach, some mashup use cases have been defined to prove the usefulness of our mashup platform and combine the personal resources of SemanticLIFE and SocialLIFE.

Use Case: Personal Finance Mashup

The first use case is dedicated to the Tim Berners-Lee's vision about combining different data resources [START_REF] W3c | W3C Semantic Web Activity[END_REF]. He has formulated this vision as follows: "The Semantic Web is a web of data.

Use case: Personalized Mashup

This use case is aiming to combine and enrich some personal data with relevant data from social networks and is formulated as follows: I want to check the events in my local calendar as well as events from my SNSs in a specific time. For a specific event, show me some famous tourist attractions in the location of that event, including some ). This use case can be realized via a personalized mashup that uses the following six widgets (as depicted in Fig. 10): ─ Calendar Events widget: retrieves personal calendar events from SemanticLIFE or SocialLIFE (i.e., Facebook events as mentioned in Section 2). This widget will return the events with relevant information (e.g. locations or organizer). ─ Tourist attractions widget: calls the third party service to return information about places based on the given input location. This service may be a query to DBpedia via its dedicated SPARQL Endpoint. ─ Geolocation converter: is a widget that converts the place address into the geolocation format for viewing in Google map. ─ Google map widget: shows the obtained places in a Google map. ─ Flickr widget: is a third party service of Flickr to search photos that match some given criteria. In this use case, the matching condition will be the name of the place. ─ Weather widget: shows the weather forecast of the given place.

Related Work

There are a number of research works that deal with part of our proposed approach: (i) expanding the scope of Semantic Desktops with social community aspects, and (ii) building semantic mashup applications to support end-users integrate and reuse their heterogeneous personal life items. Nepomuk [START_REF] Groza | The NEPOMUK Project -On the way to the Social Semantic Desktop[END_REF] is mainly enabling a more meaningful searching on the desktops and bridging the gap between Semantic Desktops and Linked Data, but does not support end-users in data mashups. DERI Pipes [START_REF] Le Phuoc | DERI Pipes: visual tool for wiring Web data sources[END_REF] have mainly focused on the data aggregation that aims at (semi-) automating mashup development, but end-users are still required to master a basic level of programming knowledge, such as specifying parameters for operators and widgets. Besides, some mashup languages (e.g. Yahoo Query Language5 , Enterprise Mashup Markup Language 6 ) are proposed for the description of programming logic and presentation of data. However, these languages are mostly not easy to understand for end-users without prior knowledge of those languages or extensive programming skills. In addition, those languages does not support ontology or customized ontology mapping yet and support only the construction and formulation of service calls and their execution.

In our semantic-based mashup framework, end-users without programming skills can easily find the right and feasible widget connections on the fly. Fig. 11 depicts the semantic-aware dataflow between widgets and depicts how the mashup platform supports the end-users to find the appropriate input/output connections. In this paper, we have developed a mashup framework where users and mashup developers can collaborate to design personalized mashups on demand. We have also proposed a mashup language that supports mapping ontologies in designing semanticaware mashup dataflows. The overall objective of this approach is aiming at overcoming the steadily increasing problem of information overload of Semantic Desktops and SNSs (i.e. SemanticLIFE and SocialLIFE in particular). To achieve this goal, the proposed solution expands the scope of our SemanticLIFE in particular and the Semantic Desktops systems in general into the Web of Data. As future work, the development of the proposed system will be continued to further improve the mashup language and the mashup framework. In this context, we will extend our widget collection to include more third party services of well-known SNSs in order to enrich both personal resources and the mashup repository. Furthermore, the development towards the automatic widget composition will be investigated. Another interesting issue is the mashup sharing which should be supported in order to enable end-users in building data mashups collaboratively in organizations/enterprises. In addition, further experiments for performance and usability evaluation should be conducted.

Fig. 4 .

 4 Fig. 4. Semantic-based mashup architecture for SemanticLIFE and SocialLIFE ─ Mashable semantic data layer: focuses on the retrieval of personal resources from SemanticLIFE and SocialLIFE, and their converting them to RDF based on the relevant domain ontologies. ─ Context & Security Policy layer: This layer is in charge of the efficient implementation of information security and privacy policies. More importantly, this layer includes context ontologies for describing personal services and the enforcement of

Fig. 5 .

 5 Fig. 5. The schema of personal resources mashup language (PRML)

Fig. 6 .

 6 Fig. 6. SPARQL query generation based on widget parameters

Figure 9 ,

 9 depicts for the personal finance mashup. To illustrate and demonstrate the feasibility of this use case, two basic widgets are used: ─ Bank Statements widget: which retrieves the bank statements of a given bank account in a specific period of time. ─ Calendar widget: which shows the details of bank statements in a single and flexible view.

Fig. 9 .

 9 Fig. 9. Personal finance mashup for showing bank statements in calendar view

Fig. 10 .

 10 Fig. 10. Design personalized mashup.

Fig. 11 .

 11 Fig. 11. Semantic-aware dataflow 6 Conclusion and Future Work

  There is lots of data we all use every day, and it is not part of the web. I can see my bank statements on the web, and my photographs, and I can see my appointments in a calendar. But can I see my photos in a calendar to see what I was doing when I took them? Can I see bank statement lines in a calendar? Why not? Because we do not have a web of data. Because data is controlled by applications,

and each application keeps it to itself". Besides, you might have many bank accounts and want to keep a record of different bank transactions from those accounts in a single view (i.e., a calendar view). For realizing this use case, we have created a specific data convertor that converts the financial data from OFX format into RDF triple and stored in the SemanticLIFE repository.

http://www.ofx.net/

http://en.wikipedia.org/wiki/Mashup_(web_application_hybrid)

http://dbpedia.org/snorql/

http://eventmedia.eurecom.fr/sparql/

http://developer.yahoo.com/yql/

http://en.wikipedia.org/wiki/EMML Wrong mashup connection, which is not allowed in mashup design Feasible mashup connection


Yi-Li Huang

Fang-Yie Leu

Ilsun You
email: ilsunu@gmail.com

Jing-Hao Yang

Building an Initialization Cipher Block with Two-Dimensional Operation and Random Parameters

Keywords: Cryptography, Block Cipher, random parameter, Two-Dimensional Operation, ICBM

In recent years, parallel computing capabilities have been more powerful than before. Consequently some block cipher standards, such as DES used to protect important electronic messages, have been cracked in the past years. Also due to the rapid development of hardware processing speeds, 3DES and AES may someday be solved by brute-force attacks. Basically, the common characteristics of these block cipher standards are that each time, when a standard is invoked, the same parent key is used to generate subkeys. The subkeys are then utilized in the standard's encryption rounds to encrypt data. In fact, the variability of the key values is quite limited. Generally, producing random parameters to encrypt data is an effective method to improve the security of ciphertext. But how to ensure the security level of using and delivering these random parameters and how to avoid information leakage have been a challenge. So in this paper, we propose a novel random parameter protection approach, called the Initialization Cipher Block Method(ICBM for short), which protects random parameters by using a two-dimensional operation and employs random parameters to change the value of a fixed parent key for block ciphering, thus lowering the security risk of a block cipher algorithm. Security analysis demonstrates that the ICBM effectively improve the security level of a protected system. Of course, this also safely protect our homeland, particularly when it is applied to our governmental document delivery systems.

Introduction

In recent years, due to the rapid development of different network technologies, many government offices, private companies, banks, etc. for environmental protection purpose send electronic documents, rather than paper documents, to people or customers to save paper consumption, of course also reducing the resource consumption for our homelands. With this achievement, information on the contrary is easily spread, delivered and obtained. Today, the Internet has been the biggest information pool, from which users can access different kinds of required data. However, when people enjoy the Internet access, attackers may also steal private information through the Internet for a variety of purposes. Currently, new attacks have been developed quickly and new vulnerabilities have been discovered frequently. So it is hard for us to completely protect private information by using IDS, IPS and firewall. Then how to use cryptographic techniques to protect such information has been a very serious and urgent issue.

In the field of data encryption, block cipher is one of the most common method to protect electronic documents from being known to hackers. The most famous and widely used block cipher standards are the Data Encryption Standard (DES)[1], Triple Data Encryption Standard (3DES) [START_REF]NIST Special Publication 800-67, Recommendation for the Triple Data Encryption Algorithm (TDEA) Block Cipher[END_REF] and Advanced Encryption Standard(AES) [START_REF]Advanced Encryption Standard (AES)[END_REF] published by National Institute of Standards and Technology (NIST). However, in 1997, DES message has been broken for the first time in public. And in 1999, the security key of DES has been broken [4]. Due to the rapid advance of hardware speed and parallel computing, securely protecting this type of fixed-key cryptography has been a technical challenge [START_REF][END_REF]. Now the block cipher standards still used in many ways to protect personal privacy and confidential information. We hope to enhance their security levels without significantly changing their original structures.

Therefore, in this study, we propose a random number protection approach, named Initialization Cipher Block Method (ICBM for short), which strengthens the security of the block cipher standards by adding random parameters, which are themselves unpredictable, to them. Security analysis shows that the random parameters as security keys can effectively rise the security level of a system since only the user who has the legitimate keys can solve those messages protected by these random parameters.

The rest of this paper is organized as follows. Section 2 introduces the most popular block cipher standards. Section 3describesthe proposed method. The security of Initialization Cipher Block Method is analyzed in Section 4.Section5concludes this paper and outlines our future studies.

Related Work

In this section, we individually introduce DES, 3DES and AES.

DES and 3DES

DES algorithm was first developed between 1973 and 1974 by International Business Machines Corporation (IBM). This proposal was then accepted and published by National Bureau of Standards (NBS), the predecessor of National Institute of Standards and Technology (NIST). It is now one of the data encryption standards. Generally, its security key is used to customize the transformation from ciphertext to plaintext, i.e., only the user who has current key can solve the ciphertext. The original length of a key is 64 bits. But only 56 bits are used to encrypt data. Other 8 bits, employed to check data parity, are abandoned after encrypting data. Due to short key length, nowadays DES is no longer a secure block cipher standard. The 3DES is then proposed to solve the short-key problem by repeating DES operations three times, as three phases, to complete its data encryption and decryption. Because users can choose different keys for each phase each time when 3DES is invoked, the length of a key is then 168(= 56 × 3) bits.

The encryption process of 3DES does not really repeat DES encryption process. In the second phase, it decrypts the data, and in the third, it encrypts the data again. In other words, an integral 3DES encryption uses DES encryption-decryption-encryption process and its decryption employs DES decryption-encryption-decryption process. If the three keys utilized in the three phases of encryption and decryption are the same, 3DES is indeed a DES.

AES

AES algorithm was published by NIST in 2001 to substitute for the DES. Its block size is 128 bits. But the key length can be 128, 192 or 256 bits. Generally, a longer key can raise a system's security level.AES operation needs a 4 × 4matrix, in which an element is 8 bits long. Due to allowing different key lengths, the encryption / decryption algorithms will repeat 10, 12 or 14 rounds and each round includes 4 steps, except the last round. The 4 steps are as follows.

Step 1: AddRoundKey.When an encryption round starts, AES generates a round key by invoking the Rijndael key schedule [START_REF]Advanced Encryption Standard (AES)[END_REF], and the key is then XORed with the underlying plaintext block.

Step 2: SubByte.In this step, an element of the 4*4 matrix is moved to its designated position according to the content of the Rijndael S-box, which provides the matrix with a nonlinear transformation.

Step 3 ShiftRow.In this step, row i left shifts i bytes, i = 0, 1, 2, 3.

Step 4: MixColumns.In a column, the 4 bytes from bottom to top are treated as the coefficients of the 1, X, X 2 and X 3 in a GF(2 8 ) polynomial. The column is first multiplied with 3X 3 + X 2 + X + 2and moduloed by X 4 + 1.

Initialization Cipher Block Method (ICBM)

In the ICBM, there is a cipher block generated before the system starts encrypting data.

The main function of this cipher block is protecting those random parameters used to encrypt the parent key. Of course, the length of the cipher block is changed if the lengths of the given key is different.

Parameters and Functions definition

In the following, we first define the parameters and functions used by the ICBM: K: The key of block ciphering. Its length can be changed to meet the key length of the standard being concerned. S-box: The S-box utilized when collaborating with DES or AES. K01: The first transient key generated by substituting for the bytes of K, following the content of S-box. K02: The second transient key generated by XORing K with K01. RND: The random parameter, the length of which is determined by the length of K.

There are many methods to generate RND, e.g., invoking a random number generation function or using the One Time Password(OTP) system provided by a programming language, like Java/C++. + 2 : A binary adder, which is a logical operator defined in [START_REF] Wei | A Secure Communication over Wireless Environments by Using a Data Connection Core[END_REF].

-2 : The inverse operation of+ 2 [START_REF] Wei | A Secure Communication over Wireless Environments by Using a Data Connection Core[END_REF]. K': The system key calculated by binary-adding RND and K02 for substituting for K to encrypt / decrypt data blocks. ICB: Initialization Cipher Block, which when delivered is placed at the position in front of ciphertext.

Encryption process of ICBM

Step 1.K is the parent key of block ciphering, i.e., 56 bits for DES, and 128, 192 or 256 bits for AES. After K is input, the proposed system will calculate the corresponding value, following the content of the S-box of the selected block cipher standard, and then generate the first transient key K01.

Step 2.Calculating the second transient key K02 by XORing K01 with K.

Step 3.Generating RND by using the example method mentioned above and then encrypting it with K01 and K02 to generate two outputs. One is K' which substitutes for K in the following encryption and decryption processes. The other is ICB which is output with the ciphertext and as stated above, is placed at the position in front of the ciphertext. In decryption process, the ICB needs to be decrypted first to obtain the random parameter RND for the following decryption, i.e., Step 3.After the generation of K01 and K02, RND and K' can be obtained by invoking the following formulas.

K ′ = RND + 2 K 02 (1)
ICB = RND + 2 K 01 (2)
RND = ICB -2 K 01 (3)
K ′ = RND + 2 K 02 (4)

Security Analysis

This section will describe the security of the Two-Dimensional Operation and Initialization Cipher Block Method.

Security of the Two-Dimensional Operation

Generally, the field of electronic cryptography only uses XOR(⊕) to encrypt / decrypt data because it is a fast and simple operation. If plaintext A and a key B are of the same length n, the probability of cracking A ⊕ Bis

In this study, we utilized another logic operator, the Binary Adder(+ 2 ), to enhance the complexities of the encryption / decryption processes of the ICBM. The security level of + 2 is the same as that of XOR [START_REF] Wei | A Secure Communication over Wireless Environments by Using a Data Connection Core[END_REF]. If a key Y of m bits in length is employed to encrypt plaintext X of also m bits long, the probability of cracking X + 2 Yis 1 2 m [START_REF] Huang | A Secure Data Encryption Method by Employing a Feedback Encryption Mechanism and Three-Dimensional Operation[END_REF].

Security of Initialization Cipher Block Method

In the ICBM, we assume that (1) the system key K is X bits in length where X is determined by the block cipher standard invoked, i.e., a key is 56 bits for DES, 56 to 168 bits for 3DES and 128 to 256 bits for AES;(2) there is a random parameter RND which is also X bits long;(3) the attacker has invalidly acquired ICB with the goal of obtaining K.

In section 3.2, the ICB is calculated by adding K01 and RND with + 2 .To crack K, the attacker needs to solve current pair of 〈RND, K 01 〉by using the invalidly acquired ICB and the anti-operator-2 .Thecracking probability P of using a brute-force method is 1 2 X .

In the cracking process, the values of all parameters are guessed by the attacker, except ICB. Even users use a fixed K to encrypt their data, and K01 and K02 are parameters of a specific length, it is hard for the attacker to solve K since K is encrypted by RND and RND is unknown to the attacker. The security level of this method is higher than that of only expanding the length of a key due to the unpredictability of RND.RND is different each time when it is generated for a session.

Conclusions and Future Studies

In this paper, we propose the ICBM which employs random parameters to increase the unpredictability of the generated ICB. This is a special design for encrypting keys before plaintext is encrypted. So it can be widely applied to different block cipher standards, like DES, 3DES and AES. What we need to do is adjusting the length of RND to meet the key length of employed standard. ICBM only uses the basic operators, like ⊕,+ 2 and -2 , and one S-box. Its consumption time is short. In the future, we will apply this concept to block cipher algorithms and design an encryption system which has the characteristics of higher randomness, security and processing speed than those of the ICBM. We would also like to derive the reliability model and performance models for the ICBM and the newly designed system mentioned so that users can predict their reliability and performance before using them. These constitute our future studies.

Fig

  Fig. 1 Generation process of ICB

n , which is also the cost of cracking a key by using a brute-force method.

Acknowledgements:

This research was partially supported by TungHai University on GREENs project, and National Science Council, Taiwan, grants NSC 100-2221-E-029-018 and 101-2221-E-029-003-MY3. This research was also in part supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2014R1A1A1005915).