Pasquale Chiaro

Simone Fischer-Hübner
email: simone.fischer-huebner@kau.se

Thomas Gross
email: thomas.gross@newcastle.ac.uk

Stephan Krenn

Thomas Lorünser

Ana Garcí

Andrea Migliavacca
email: andrea.migliavacca@cnt.lispa.it

Kai Rannenberg
email: kai.rannenberg@m-chair.de

Daniel Slamanig

Christoph Striecks

Thomas Groß

Ana Isabel Martínez Garcí

Alberto Zanini
email: alberto.zanini@lispa.it

Secure and Privacy-Friendly Storage and Data Processing in the Cloud

Keywords:

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Storing, sharing, and processing data in the cloud play vital roles in many everyday scenarios, ranging from private data vaults and company backups over identity and access management (IAM) to eHealth and eBusiness. However, besides the many benefits of the cloud setup such as cost-effectiveness and scalability, many of these applications pose very high security and privacy requirements to the solutions in use as data owners have no control over how their data is used once it is released to the cloud. Consequently, a large body of work on privacy-enhancing technologies has been proposed by the academic community, and many results have reached a high maturity level; however, as pointed out by Lorünser et al. [START_REF] Lorünser | Agile Cryptographic Solutions for the Cloud[END_REF], a large fraction of these results is purely academic and does not sufficiently address the needs of users and service providers, and thus does not get adopted in the real world.

The ambition of the two large-scale European Horizon 2020 (H2020) research and innovation actions prismacloud8 and credential 9 is to close this gap for certain cryptographic primitives, by developing promising candidates for integration into commercial cloud offerings. This is achieved by involving all relevant stakeholders in the design process. Based on their inputs, a careful selection of cryptographic technologies was made, and efficient and secure first prototype implementations were developed. To showcase the usability and usefulness of these prototypes, they were then integrated into multiple pilot scenarios coming from the real world.

After already having held independent workshops at the IFIP Summer Schools 2015 [START_REF] Alaqra | Signatures for Privacy, Trust and Accountability in the Cloud: Applications and Requirements[END_REF] and 2016 [START_REF] Karegar | Opportunities and Challenges of CREDENTIAL -Towards a Metadata-Privacy Respecting Identity Provider[END_REF], the two projects organized a joint workshop in 2017, in order to raise awareness of their solutions, and to receive feedback and inputs on the developed pilots. During the remaining runtime of the projects, this feedback will be used to further improve the developed tools in order to guarantee that they indeed serve the needs of real users and cloud service providers, and to adequately address any concerns, specific requirements, or ideas.

This paper summarizes the content of this workshop, and gives an overview of the discussions with students and senior researchers from different domains, legal experts, and other relevant non-academic stakeholders.

Outline

This document is structured as follows. After briefly explaining the challenges of identity management in the cloud in Section 2, Section 3 briefly summarizes the main ambitions of the two projects credential and prismacloud. Section 4 then gives detailed descriptions of five pilots executed in the two projects, two from credential and three from prismacloud. A summary of the feedback given by the workshop participants and the projects' advisory board members is then given in Section 5. Finally, we briefly conclude in Section 6.

Cloud Privacy & Identity Management

In the following, we give an introduction to privacy-friendly and trustworthy identity management. We discuss the privacy and security issues of typical federated identity management architectures, namely over-identification and the "Calling Home" problem, and present possible solutions to both of them.

Over-identification occurs, when users need to present credentials, that contain more information than needed to justify the respective access claim, e.g., when an ID card is presented to prove legal age often the precise birth date is presented, while a certified Boolean statement, that the person is of legal age, would be satisfactory and would avoid misuse of the birth date information. The "Calling Home" problem is caused by credentials, that are always double-checked with the issuer, which causes a lot of information there, which user is using which credentials for which service at which point in time. It can also be caused by situations in which users need to ask for a credential on the spot, exactly when they need it.

Technical solutions for addressing these issues include the following: partial identities, attribute-based credentials (especially Privacy-ABCs) and redactable signatures for cloud identity management: Partial identities and identities as such are defined in ISO/IEC 24760 [START_REF]A Framework for Identity Management -Part 1: Terminology and Concepts, Part 2: Reference Framework and Requirements, Part 3: Practice[END_REF] as a "set of attributes related to an entity". Partial identities support the building of identity management systems, that enable users to select the appropriate attributes for the respective situation and so help against over-identification. Attribute-based credentials (especially Privacy-ABCs) enable the user to have the relevant attributes certified without having to recur to the original certifiers of the attributes: If a set of attributes is certified in a Privacy-ABC the certified users can choose their own subset of attributes as needed and derive the certificate themselves from the original one. More details on the nature and the trialling of Privacy-ABCs in real life scenarios can be found, e.g., in Rannenberg et al. [START_REF]Attribute-based Credentials for Trust: Identity in the Information Society[END_REF]. Redactable signatures for cloud identity management as developed in the credential project enable the editing of encrypted credentials (e.g., to protect them when stored in a cloud-based identity management system). So redactable signatures enable the editing of Privacy-ABCs.

Further approaches for a more privacy-friendly Internet and respective cloud services are:

-Decentralisation; -Minimum disclosure; -Strong sovereign assurance tokens (e.g. smart cards, if appropriate mobile devices).

Project Overview

The following section briefly explains the approaches of credential and prismacloud to address them.

Privacy-Friendly IAM with credential

credential is an innovation action dedicated to the design and implementation of a privacy-preserving platform for sharing of authenticated data, including identity and access management scenarios, thereby directly addressing the problem of overidentification and partially adressing the "calling home" problem [START_REF] Hörandner | CRE-DENTIAL: A Framework for Privacy-Preserving Cloud-Based Data Sharing[END_REF][START_REF] Kostopoulos | Towards the Adoption of Secure Cloud Identity Services[END_REF].

The security and privacy of the developed platform, the so-called credential Wallet, is mainly based on two cryptographic building blocks, redactable signatures and proxy re-encryption. Redactable signatures [START_REF] Johnson | Homomorphic Signature Schemes[END_REF] allow the signer to define parts of the message which can later be blanked out by any party knowing the message and the signature. That is, any party, not requiring access to the secret signing key, can later remove (subsets of the) redactable parts of the message and simultaneously adapt the signature such that the obtained signature still certifies the authenticity of the revealed information. On the other hand, proxy re-encryption [START_REF] Blaze | Divertible Protocols and Atomic Proxy Cryptography[END_REF] is an extension of traditional public key encryption schemes, where a dedicated third party (the proxy) can transform ciphertexts encrypted for a user A to ciphertexts encrypted for a user B, without itself every gaining access to the plain data. This is achieved by letting A use his secret key and (depending on the concrete scheme being used) B's public or secret key to compute a so-called re-encryption key, which is then sent to the proxy and can only be used for re-encryption but not decryption.

The overall approach of credential now is as follows. Users can obtain certificates on personal attributes from an issuer who signs them using a redactable signature scheme. The encrypted attributes together with the signature are then uploaded to the credential Wallet. Furthermore, when a user first wants to authenticate himself towards a specific service provider, the user computes a reencryption key from his own public key to the service provider's public key, and stores this re-encryption key in his account at the credential Wallet. Now, for subsequent authentications, the Wallet re-encrypts only those attributes that the user does chooses to reveal to the service provider and redacts the remaining ones. By doing so, the service provider will still be convinced that the revealed attributes have not been altered, hereby solving the problem of over-identification similar to attribute-based credentials systems [START_REF] Chaum | Untraceable Electronic Mail, Return Addresses, and Digital Pseudonyms[END_REF][START_REF] Chaum | Security Without Identification: Transaction Systems to Make Big Brother Obsolete[END_REF][START_REF] Camenisch | Formal Treatment of Privacy-Enhancing Credential Systems[END_REF]. The approach is also illustrated in Figure 1.

Fig. 1: The credential approach, adapted from Karegar et al. [START_REF] Hörandner | CRE-DENTIAL: A Framework for Privacy-Preserving Cloud-Based Data Sharing[END_REF].

The credential approach is not directly susceptible to the "calling home" problem as the issuer is not contacted upon authentication. However, the credential Wallet as a central entity is contacted upon every authentication process, and therefore learns which service a user accesses at which time. A partial solution to this problem, where the Wallet only learns that a user is authenticating to some service but not to which one, was recently suggested by Krenn et al. [START_REF] Krenn | Towards Attribute-Based Credentials in the Cloud[END_REF].

The feature set and usability of the credential Wallet is demonstrated using use case scenarios from the domains of eGovernment, eBusiness, and eHealth, cf. also Section 4.

prismacloud Overview and Applications

prismacloud is a research and innovation action dedicated to enabling secure and trustworthy cloud-based services by improving and adopting novel tools from cryptographic research [START_REF] Lorünser | Towards a New Paradigm for Privacy and Security in Cloud Services[END_REF][START_REF] Lorünser | Cloud Security and Privacy by Design[END_REF]. Cloud computing raised the need for application of cryptography to be more secure and privacy-friendly. However, the adoption of cryptography for modern information and communication (ICT) technologies is not hampered by the lack of technical feasibility, but more by accompanying factors like usability, missing knowledge in IT security community, and regulation.

The prismacloud approach is to propose a layered architecture of secure and trustworthy cloud-based services that utilizes strong and novel cryptographic primitives and tools to be adapted to several real-world applications. This layered approach is illustrated in Figure 2.

Layer 1 (Primitives). prismacloud is focusing on a broad range of cryptographic primitives on the lowest layer, including attribute-based credential systems for privacy-preserving user authentication [START_REF] Chaum | Untraceable Electronic Mail, Return Addresses, and Digital Pseudonyms[END_REF][START_REF] Chaum | Security Without Identification: Transaction Systems to Make Big Brother Obsolete[END_REF][START_REF] Camenisch | Formal Treatment of Privacy-Enhancing Credential Systems[END_REF], secret sharing for secure distributed storage of data at rest [START_REF] Shamir | How to Share a Secret[END_REF][START_REF] Blakley | Safeguarding cryptographic keys[END_REF], malleable signature schemes for controlled modifications of authenticated data [START_REF] Johnson | Homomorphic Signature Schemes[END_REF][START_REF] Ahn | Computing on Authenticated Data[END_REF][START_REF] Haber | Efficient Signature Schemes Supporting Redaction, Pseudonymization, and Data Deidentification[END_REF][START_REF] Camenisch | Chameleon-Hashes with Ephemeral Trapdoors -And Applications to Invisible Sanitizable Signatures[END_REF], or graph signatures for topology certification [START_REF] Groß | Efficient certification and zero-knowledge proofs of knowledge on infrastructure topology graphs[END_REF]. Malleable signatures are a super set of redactable signatures, that allow for even advanced functionality besides the redaction of signed messages. Secret sharing on the other hand allows for secure distribution of sensitive data. Thereby, the message is split into shares and distributed to many cloud databases. Inherently, the system has redundancy in the sense that only a fraction of the cloud databases is needed to reconstruct the message. Graph signatures encode graph data structure into the underlying digital signature scheme in a way that for all components of a graph (i.e., edges, vertices, labels), proof-of-knowledge properties can be stated. Together with attribute-based credentials, malleable signatures tackle the over-identification and "Calling home" problems. Layer 2 (Tools). On the next layer, the primitives from Layer 1 are included into more complex tools. For example, attribute-based credentials and malleable signatures are used as building blocks for Flexible Authentication with Selective Disclosure and Verifiable Data Processing, respectively. Further, the Topology Certification tool is presented in more detail in Section 4.5. Fig. 2: The layer approach of prismacloud from Lorünser et al. [START_REF] Lorünser | PRISMACLOUD Tools: A Cryptographic Toolbox for Increasing Security in Cloud Services[END_REF].

Layer 3 (Services). On the service layer, the tools from the Layer 2 are the building blocks for more advanced functionalities. For example, Flexible Authentication with Selective Disclosure and Verifiable Data Processing are used as building blocks for Privacy Enhancing Identity Management (IDM) and Verifiable Statistics, respectively. Furthermore, Secure Archiving is derived from Secure Object Storage (cf. also Section 4.4) and directly serves as a example for the Decentralization approach mentioned in Section 2. Layer 4 (Applications). The application layer deploy the services from Layer 3 in real-world scenarios such as Smart City, eGovernment, and eHealth, cf. also Section 4.3.

Note that in contrast to Layers 1 and 2, almost no cryptographic knowledge is needed any more on Layers 3 and 4, making them also accessible to software developers and product designers without requiring deep mathematical background.

prismacloud also has a strong focus on human computer interaction (HCI) design patterns, the implementation of modular and reusable software libraries, and the design of cloud services that can be seamlessly integrated into existing software applications via the layered approach. This holistic approach enables prismacloud to achieve its main ambition, namely to enable end-to-end security and privacy for cloud users without negatively impacting usability neither for users nor for service providers.

Pilots and Discussions

The following section introduces some of the pilots designed, implemented, and executed within prismacloud and credential. Furthermore, it summarizes the feedback, questions, suggestions, and concerns received from the workshop participants, who were able to get hands-on experience of the pilots during the IFIP Summer School 2017.

eGovernment Pilot (credential)

The credential eGovernment pilot focuses on identity management to authenticate citizens towards services provided by public authorities. Based on standardized protocols such as SAML or OpenID Connect, the service provider can request authentication and identity attributes from the credential Wallet. By requesting the user's consent for granting the service provider access to the requested data, the user is given full transperancy and control over which data is requested and revealed; furthermore, because of the encryption technology being used, the credential Wallet never obtains access to the user's attributes in an unencrypted form. The pilot not only enables authentication via national eID solutions, but also cross-border authentication according to the eIDAS regulation, aims at high interoperability with existing authentication protocols, and minimizes the integration effort on the service provider's side.

A bit more precisely, the eGovernment pilot considers a user owning a credential account that already contains a set of authentic data items. The user wants to authenticate himself towards a service hosted by Lombardia Informatica S.p.A. (LISPA), a public-capital service company in northern Italy. Specifically, we assume that the user wants to authenticate himself towards SIAGE, a web portal used to request tax breaks and other types of fiscal advantages. When browsing to the login page, the user has the option to choose credential as an identity provider, and is then redirected to an URL published by the Ope-nAM component in charge of initiating the authentication flow according to the OAuth2 standard. The user then receives a notification on his mobile phone listing all the required and optional attributes SIAGE wants to access for authentication and the subsequent process, and can decide whether or not to disclose these values, cf. Figure 3. In case the user gives his consent, the requested data Fig. 3: eGovernment pilot. is re-encrypted by the credential Wallet and sent to SIAGE, who can then decrypt the data and verify its authenticity.

eBusiness Pilot (credential)

Many business processes can nowadays be performed online. However, there is often a trade-off between security and usability: that is, systems that are easy to use often do not sufficiently protect security and privacy, while safer processes often partially sacrifice usability. The credential eBusiness pilot addresses some of the most often performed processes and provides privacy-friendly, secure, and usable solutions for authentication and Single Sign-On (SSO), purchase processes and online form subscription, and forwarding of encrypted communication. The first scenario is strongly related to the authentication use case in the eGovernment pilot presented in Section 4.1. The second scenario reliefs users from having to enter all their personal information like name, date of birth, or address everytime they subscribe to a service; rather, the user's information that is already stored in the credential Wallet can automatically be filled into the registration forms. In the following we will put our main focus on the third scenario on encrypted communication.

InfoCert is an Italian organization offering trust based business solutions for organisations and businesses to interact with customers and citizens. Among others, InfoCert offers Legalmail, an email service realizing legally binding mail exchange. The goal of credential is to add an end-to-end encryption layer to Legalmail. However, due to the legal properties of the communication it is important to support proper mail forwarding possibilities for such mails. This is because an email is legally considered delivered once it reaches the recipients mailbox, independent of whether it was read or not; thus, in the case of a longer Fig. 4: eBusiness pilot. absence, it might happen that the receiver otherwise misses important deadlines or similar. However, using the standard forwarding capabilities of the mail server is not sufficient for encrypted emails for obvious reasons: either, the delegatee cannot decrypt the received message, or the original receiver would have to share his secret keys with the substitute. Within credential, this problem is addressed by using proxy re-encryption [START_REF] Blaze | Divertible Protocols and Atomic Proxy Cryptography[END_REF], where the receiver can deposit a re-encryption key that allows the mail server to translate the cipher text into an encryption under the delegatee's public key without itself learning the plain message.

After explaining the scenario, interactive mockups were used to show how Legalmail users can setup the encrypted mail forwarding within a slightly extended Legalmail app. Figure 4 shows how a user first accesses into the setup section, then selects "filter setting" and "add new filter", finally defines the forwarding rules.

Discussion and feedback. Considering that Legalmail was new to all workshop participants, the received feedback was quite interesting: both technical and not technical attendees agreed about the perceived value of a legally binding communication. They also founded coherent the need to enhance the security in sharing some kind of sensitive information. Furthermore, several concrete suggestions on how to improve the user interfaces and experience were made by the workshop participants.

Smart-City Pilots (prismacloud)

During the tutorial, demos of two prismacloud pilots applied within the smartcity environment SIMON 10 were shown. Each of them integrates one secure cloud service, developed inside the prismacloud project. SIMON is another European project dedicated to remove and prevent barriers that cause problems for persons with disabilities when using products, services and public infrastructure. One goal of SIMON is to ensure that only disabled persons are able to park in reserved lots in a city. Instead of only putting the parking card into the car, the disabled person first uses the SIMON mobile application and the smart-parking card for marking the location in which he/she is parking. As parking cards are very easy to duplicate, SIMON help authorities and end users to fight fraud, because operators can check if the smart card is duplicated. In Figure 5, screenshots of the end-user's mobile parking application are given. The first demo showed the prismacloud Encryption Proxy (EP) service integrated in SIMON. Since SIMON is in the cloud, the first problem it deals with is the treatment of sensitive data. SIMON is managing sensitive data such as personal information, which do not have to be accessible to other people except for the dedicated end users. This EP service encrypts sensitive data on the fly, leaving non-sensitive data not encrypted. Therefore, the messages and the information stored on the database contain non-sensitive data in clear text and sensitive data in encrypted format.

The second demo showed the integration of Privacy Enhancing Identity Manager (PIDM) service in SIMON. Another problem of SIMON in the cloud is that the user's personal identifier is sent in clear for each operation, so users are identified. This is a big problem due to legal implications. In SIMON, city areas or neighborhood are defined and users have permissions to park in lots belonging to those areas that they belong to. The PIDM has been integrated in order to anonymize those operations. The user's identifier is replaced in messages by a proof of belonging to the area in which the lot is; thus, the system does not know which user is going to park. It only knows that a user which has permission for the area is going to park. The proof is generated by cryptographic mechanisms in the service, i.e., by means of suitable group signatures [START_REF] Derler | Fully-anonymous short dynamic group signatures without encryption[END_REF].

Discussion and feedback. During the presentation, students were very interested in the two demos and some technical question were asked. The first point of discussion was where personal data or private keys have to be stored and it was necessary to explain that the private keys belong to the end user, so they are generated and stored on the mobile memory, and the smart-card only has the firmed message in order to be checked by the parking operator. The second point was related to how to decide what is sensitive in any application. In the case of SIMON is mainly the user's personal information. Finally, students appreciated that all these cryptographic resources have use in an practical environment, due they usually deal with theoretical problems.

Distributed Storage Pilot (prismacloud)

During the tutorial the prismacloud solution for secure object storage in distributed multi-cloud settings [START_REF] Lorünser | Towards a New Paradigm for Privacy and Security in Cloud Services[END_REF] was shown. In order to protect data at rest in the cloud, the project builds on secure information dispersal, i.e., secret sharing techniques, which achieves security, integrity and availability at the same time in a very flexible and efficient way [START_REF] Krenn | Batch-verifiable Secret Sharing with Unconditional Privacy[END_REF][START_REF] Demirel | Efficient and Privacy Preserving Third Party Auditing for a Distributed Storage System[END_REF].

To give users easy access to the developed technologies, a secure archiving service (SA or SAaaS) specifically tailored to the needs of trustworthy backups was implemented [START_REF] Lorünser | PRISMACLOUD Tools: A Cryptographic Toolbox for Increasing Security in Cloud Services[END_REF]. Its functional key features were motivated from our eGovernment use case and it has been designed to support most commonly encountered cloud backup scenarios. The general idea of the secure archiving service is to provide an extremely reliable and secure storage back-end which can be easily hosted and used. The archiving use case focuses on data retention over long time periods and can even provide full quantum safe security when combined with adequate transport layer encryption, e.g., like in [START_REF] Lorünser | Security processor with quantum key distribution[END_REF][START_REF] Neppach | Key management of quantum generated keys in IPSEC[END_REF].

The prototype provides compatibility with multiple providers and legacy systems to support hybrid cloud storage scenarios which integrate local storage with public cloud offerings in a seamless way. Therefore, the Simple Storage System (S3) industry standard was chosen as main API and the service was made compatible with this standard on both sides, the back-end provider side as well as the front-end client side. Additionally, because we avoid the use of active components on the provider side, deployment can be very straight forward [START_REF] Happe | In: Malicious Clients in Distributed Secret Sharing Based Storage Networks[END_REF].

Overall, the service acts as a proxy [START_REF] Happe | The Archistar Secret-Sharing Backup Proxy[END_REF] and the basic architecture is shown in Figure 6a as well as the data flow model in Figure 6b. It is part of the Archistar software framework [START_REF] Lorünser | ARCHISTAR: Towards Secure and Robust Cloud Based Data Sharing[END_REF] and supports the following main features: increased data privacy and availability, prevention from vendor lock-in, keyless (credential based) operation, reduced data remanence, long-term security support, support for multimodal encryption, support for remote auditing, plug-in replacement for legacy systems.

Discussion and feedback. During prototype presentation the audience was particularly impressed by the ease of use and integration of the provided solution and saw a great potential for exploitation. Additionally, they recommended to think about a version where no single point of trust exist, if this is possible. They also liked the idea of making the software available as open source software, such that it can be reviewed by a larger audience. Furthermore, one participant mentioned that similar technology is already used in commercial high-end storage solutions for data centers, but no solutions for broad adoption or multi-cloud configurations exist, especially for small end medium enterprises, are known. Finally, the participants also recommended to look into the field of mix networks and the security modeling used there, which has many similarities that may be transferred to multi-cloud approaches.

Besides feedback on the technical integration, we were also interested in feedback on the perception of security in systems based on data splitting. In its very pure nature, the data security is solely based on non-collusion of cloud providers, which is a different security model from what we typically use today. During the discussions of the demo we therefore tried to find suitable configurations for such storage systems and also discussed the necessity of an additional cryptographic layer to protect from collusion attacks. The feedback was very valuable for us and will be included into the configurations guidelines we are currently preparing in prismacloud.

Infrastructure Certification Pilot (prismacloud)

The tutorial workshop offered an introduction to the Infrastructure Certification (IA) of prismacloud. The overall goal of the IA work is to enable the capacity to certify infrastructures, such as virtualized infrastructures in the Cloud in such a way that their security properties can be proven to others without disclosing sensitive data. Therein, this work follows the principles of Confidentiality-Preserving Security Assurance.

The core technical contributions presented in the workshop are the Topology Certification tool, called TOPOCERT, its underlying Graph Signature library and a demonstration functionality to prove the separation of machines by geoseparation, which we shall discuss in turn.

Confidentiality-preserving topology certification was first proposed in the work by Groß [START_REF] Groß | Efficient certification and zero-knowledge proofs of knowledge on infrastructure topology graphs[END_REF]. The core idea of this proposal is that an Auditor could issue a certificate on the topology on an infrastructure to the Provider, which is usable for subsequent zero-knowledge proofs of knowledge on a wide range of security properties. This work is founded on earlier research on topology-based security assurance [START_REF] Bleikertz | Automated Information Flow Analysis of Virtualized Infrastructures[END_REF][START_REF] Bleikertz | Cloud Radar: near real-time detection of security failures in dynamic virtualized infrastructures[END_REF][START_REF] Bleikertz | Proactive security analysis of changes in virtualized infrastructures[END_REF], which analyzes the configuration of infrastructures to determine components, sub-systems and their inter-connectivity and derives a graph representation for a security analysis. Confidentiality-preserving security assurance takes this method one step further in signing the graph representation for further proof protocols.

The graph signing and corresponding zero-knowledge proofs of knowledge are facilitated by a dedicated graph signature library. It realizes the cryptographic protocols between a Signer and a Recipient of graph signatures as well as between a Prover and a Verifier of the corresponding proofs of knowledge. Graph signatures were proposed by Groß [START_REF] Groß | Signatures and efficient proofs on committed graphs and NP-statements[END_REF] as a means to encode graph data structures into an underlying digital signature scheme, such that the components of the graphs, the vertices, edges and labels are still accessible to proofs of knowledge. In a first construction, this is facilitated by employing the Camenisch-Lysyanskaya signature scheme [START_REF] Camenisch | A signature scheme with efficient protocols[END_REF] and using techniques first introduced for the efficient encoding of credential attributes [START_REF] Camenisch | Efficient attributes for anonymous credentials[END_REF][START_REF] Camenisch | Efficient attributes for anonymous credentials[END_REF]. The first construction for graph signatures [START_REF] Groß | Signatures and efficient proofs on committed graphs and NP-statements[END_REF] has still a number of short-comings, most notably requiring the signing of one certificate per proof. However, it shows intriguing properties in that the signatures are general in that they could be used to answer a wide range of questions not known at signing time and expressive in that they could encode arbitrary statements from NP languages.

The tutorial workshop included an example application for geo-location separation [START_REF] Groß | Geo-location separation of virtualized systems[END_REF]. The idea for that is that the Auditor could certify the physical geo-location of the physical hosts. In this case, we might think of the Auditor as a tamper-proof appliance, which can obtain a secure fix on its current geo-location. In this case, the Auditor could, for instance, label the physical machines of an audited infrastructure with labels denoting the UN countries these machines are located in. The geo-location separation proof protocols executed by the Prover and the Verifier would then convince the Verifier that the machines are in at least k different countries, without disclosing which countries are involved.

Advisory Board Feedback

The tutorial workshop ended with a panel of Advisory Board members of the two projects that provided feedback and suggestions for future directions and topics to be addressed by the projects. While in general the panelists appreciated the work presented, they raised a few issues.

One issue mentioned was that a cloudified approach as provided by the credential project relies on one central server, which even if all data are encrypted, still needs to be trusted, as it can monitor all traffic data, is able to derive meta information from them (e.g., it can profile the users' usages of different services -see also [START_REF] Karegar | Opportunities and Challenges of CREDENTIAL -Towards a Metadata-Privacy Respecting Identity Provider[END_REF]) and represents one single point of failure. For users it might not be clear whether this cloudified approach can be fully trusted and it remains a challenge to communicate the trust assumptions to the users. Further research should also address the questions what is meant by trust and distrust in the whole service solution. For this, the complete process of gaining, losing and reestablishing trust in the service needs to be considered.

Furthermore, it was discussed that Open Source (such as prismacloud's Archistar) would often be seen as a means for gaining user trust, as the code is openly published and can be reviewed by the users, and that it could be easier for the projects to put their solutions as open source into practice. However, it needs to be considered also that according to the EU General Data Protection Regulation (GDPR) it is not the software producer who is responsible for achieving Data Protection by Design. Pursuant Art. 25 GDPR, the responsibility for the implementation of appropriate technical and organisational measures, which are designed to implement data protection principles, rather lies with the controller. Art. 25 (3) mentions that approved certification mechanisms pursuant to Article 42 may be used as an element to demonstrate compliance of the controller with the Data Protection by Design requirements, and such privacy certification schemes could also be means for gaining or increasing user trust.

Another comment by the Advisory Board members concerned prismacloud's Smart City parking app. It was pointed out that restricting and framing it as an app for booking parking places for disabled users could mean that users that are installing and using the app could be easily associated as handicapped and thus be discriminated. Hence, the app should have a wider framing and should also provide features that would be useful also for non-disabled users.

Finally, it was discussed and emphasized that in practice "pretty good" usable cryptographic and privacy solutions can already be very helpful for end users. Hence, the projects research should not only try to achieve provably secure solutions, but should also look at practical and usable solutions that provide good enough security for the majority of use cases.

Conclusion

Storing, sharing, and processing sensitive data in untrusted cloud environments is the central goal of the H2020 projects credential and prismacloud. This tutorial paper summarizes given presentations and demonstrations within a workshop at IFIP Summer School 2017 co-organized by both projects. In particular, the workshop aimed at introducing the necessary background knowledge and presenting prototype demonstrations of credential and prismacloud to a wider academic audience (e.g., students, senior researcher) as well as to participant from the non-academic field (e.g., public data protection agencies).

To this end, we briefly introduced the two projects and described the talks given within the workshop. Furthermore, all demonstration pilots were presented and findings discussed. Valuable feedback was gathered from the advisory-board members to enhance the further projects' development.

Fig. 5 :

 5 Fig. 5: SIMON application for end users.

Fig. 6 :

 6 Fig.6: High-Level network system overview and example data-flow from backup clients to the cloud storage servers.

https://prismacloud.eu/

https://credential.eu/

http://simon-project.eu/

The projects contributing to this work have received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644962 (prismacloud) and 653454 (credential).


Sophia Kununka
email: sophia.kununka@postgrad.mbs.ac.uk

Nikolay Mehandjiev
email: n.mehandjiev@manchester.ac.uk

Pedro Sampaio
email: p.sampaio@manchester.ac.uk

A Comparative Study of Android and iOS Mobile Applications' Data Handling Practices Versus Compliance to Privacy Policy

Keywords: Mobile applications, Privacy policy, Compliance

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Mobile applications (apps) handle unprecedented quantities of user data. App users offer or entrust diverse personal data to organizations and traders. The data provided by users may be sensitive such as personally identifiable information (personal data) which is data that can be linked back to the owner or source for example; user name, email, telephone number, gender, age, social security number, card number etc. [START_REF] Sarma | Android permissions: a perspective combining risks and benefits[END_REF]. In contrast, non-personal data is deemed unidentifiable data and can be aggregated for various purposes. User data is provided with the confidence that users' data privacy (information privacy) will be maintained by limiting data utility to the specified purposes. Notwithstanding, gaps have been observed in privacy practices as research shows the fact that apps can communicate users' personal data to third parties without users' knowledge or consent [START_REF] Thurm | Your Apps Are Watching You[END_REF].

While a range of approaches have been used in an endeavour to address non-consented use of users' data, a key focus has been on the provision of privacy policies. A privacy policy is a set of rules, or statements that specify which processing and sharing practices are permitted for different types of data collectable from the end user [START_REF] Papanikolaou | Refinement checking for privacy policies[END_REF].

According the General Data Protection Regulation [START_REF] Directive | EUR-Lex[END_REF], privacy policies are a means for data controllers to inform data subjects (end users of the app) about what personal data will be collected and for what purpose and as such are a key element in ensuring informed consent. As such, they help to dispel users' anxieties about the revelation of personal data [START_REF] Westin | Privacy and Freedom[END_REF]. Further, privacy policies build user trust and enable app to achieve regulatory compliance. However, several studies [START_REF] Ai Anton | Analyzing Web site privacy requirements using a privacy goal taxonomy[END_REF] [7] [START_REF] Sadeh | Understanding and capturing people's privacy policies in a mobile social networking application[END_REF] indicate that privacy policies have been found to be inadequate in their attempt to preserve user privacy. For instance, privacy policies have been critiqued for being "far too long and complex" [START_REF] Cellan-Jones | BBC[END_REF]. Similarly, while provision of privacy policies are an important step in reinforcing user data privacy, the extent to which this endeavour is successful is largely dependent on an app's adherence or compliance to its own privacy policy. Moreover, privacy related challenges have been identified in apps that run on both Android and iOS app platforms even while they rank top in popularity [START_REF] Liu | Smartphone privacy in mobile computing: Issues, methods and systems[END_REF]. Android apps present users with a permission list, during installation, on a take-it-or-leave-it basis with no specific reason for its requirement unless if user consults the provided privacy policy. This could facilitates possible privacy abuse as apps seek to access as much user data as possible irrespective of whether or not it's required for the apps' functionality [START_REF] Wei | Permission evolution in the Android ecosystem[END_REF]. A study [START_REF] Grace | Unsafe Exposure Analysis of Mobile In-App Advertisements[END_REF] found that in spite of a user's call history having no direct influence on the ads a user might want, there were Ad libraries that collected and conveyed this information to the internet. Further, Ad libraries have been observed to engage in permissions usage that could introduce privacy risks [START_REF] Book | Longitudinal Analysis of Android Ad Library[END_REF]. Efforts to address privacy abuse led to the development of Android's Marshmallow version [START_REF]Ashnis: noeticforce[END_REF] which operates on a similar principle to iOS. In both cases, requests for specific permissions are made as and when they are needed using a pop up message that allows users to either accept or deny the permissions [START_REF] Hoffman | How -To Geek[END_REF].

Comparing how easy it is to understand the way permissions are on both platforms, it is observed that while Android is more informative in terms of detail, it uses more technical terminology than iOS which could impact on extent of user understanding [START_REF]Ashnis: noeticforce[END_REF]. Nonetheless, [START_REF] Benenson | Android and iOS users' differences concerning security and privacy[END_REF] argues that privacy risks arise because users often lack the full picture of information that could be collected and the possibilities of using it in ways that are unknown to them. Security-wise, the android apps present more risk while the iOS apps tend to be safer [START_REF] Felt | A survey of mobile malware in the wild[END_REF]. However, [START_REF] Benenson | Android and iOS users' differences concerning security and privacy[END_REF] stresses that there is reduced privacy awareness and fear among iOS users. Notwithstanding, iOS apps have been found to be vulnerable in some instances [START_REF] Seriot | iPhone Privacy[END_REF] [19] and the vetting process implemented by Apple to ensure that iOS apps are aligned with Apple's privacy critiqued for its lack of transparency [START_REF] Egele | PiOS: Detecting Privacy Leaks in iOS Applications[END_REF].

Companies that do not take user privacy concerns into consideration for instance when using personal data developing profiles that facilitate tailoring of Ads, are likely to counter public backlash [START_REF] Liu | An Examination of Privacy Policies in Fortune 500 Web Sites[END_REF]. Moreover, whereas regulation requires apps to provide privacy policies, the extent to which these policies are contractual is debatable as they change as and when the firm decides. However, increased privacy confidence increases online success. Users want government involvement through means such as enacting laws that protect the privacy of personal information collected through apps. Regulatory bodies such as the Federal Trade Commission (FTC) in the US and the European Data Protection Regulation [START_REF] Directive | EUR-Lex[END_REF] demand that users are informed of the data gathered by apps, why it is collected and that opt out provisions are made for users [START_REF] Wetherall | Privacy revelations for web and mobile apps[END_REF]. Nonetheless, the existence of government regulation does not imply that companies comply with the requirement. This is underpinned by a recent study by several authors [START_REF] Van-Alsenoy | [END_REF] in which a critical analysis of Facebook's revised policies and terms was conducted based on the EU Data Directive. The findings of the study indicate that Facebook engages in questionable privacy practices. As such, there is need to ascertain the extent of apps compliance to their privacy as a pointer to the extent to which users would have confidence in using the apps' service.

A study that examined the personal, behavioural and location data from 110 apps indicates that Android and iOS apps generally transmit sensitive data to 3.1 and 2.6 third party do-mains respectively [START_REF] Zang | Who Knows What About Me? A Survey of Behind the Scenes Personal Data Sharing to Third Parties by Mobile Apps[END_REF]. Our work seeks to extend that study by exploring the apps data handling practices verses compliance of apps to their privacy policy. As such, our study conducts an investigation into whether the user data collected and disseminated by apps to third party domains is matches their privacy policies. The analysis was conducted based on a privacy compliance comparison between Android and iOS apps as these are the dominant app platforms, by exploring the extent to which apps adhere to their stated privacy policies and, the resulting effects of apps' data handling practices.

Our study seeks to answer the research question: Do mobile application privacy policies match their practices? To answer this question we consider mobile applications from the two dominant mobile application platforms i.e. Android and iOS. The remainder of the paper is organized as follows: related work is presented in section 2, followed by the research method in section 3, after which the findings of the study are presented in section 4. In section 5, a discussion on the findings is presented and Section 6 sums up the paper with conclusions and subsequent work.

Related Work

Related research conducted [START_REF] Sunyaev | Availability and quality of mobile health app privacy policies[END_REF] has focused on availability, scope and transparency of mobile app privacy policy. That study found that two-thirds of the apps' contained content that was not directly related to the app. Further, information privacy practices were not clear. However, the study was limited to health. In another health privacy policy related study, [START_REF] Antón | A requirements taxonomy for reducing Web site privacy vulnerabilities[END_REF] analysed website related vulnerabilities based on 23 website policies using goal mining techniques for the extraction of pre-requirements goals from post-requirements text artefacts from which a taxonomy was developed. Research [START_REF] Rosen | Approfiler: a flexible method of exposing privacyrelated behavior in android applicaitons to end users[END_REF] argue that the permissions system should be more fine grained and develop an sought to enhance user understanding by providing a mechanism of equipping users with information required before application downloads. Further work [START_REF] Sarma | Android permissions: a perspective combining risks and benefits[END_REF] explored the practicability of combining permissions and app requests in advising using on whether the risk of installing an app outweighs the expected benefits.

More so, another study presented by [START_REF] Zang | Who Knows What About Me? A Survey of Behind the Scenes Personal Data Sharing to Third Parties by Mobile Apps[END_REF], used 110 widely used Android and iOS apps to explore the different user data that apps conveyed to third parties. Using an iPhone 5 and a Samsung Galaxy S3, HTTP and HTTPS traffic from the apps was captured using a proxy and examined for personally identifiable data. As a control, push notifications were blocked so as not to allow apps to transmit data in background when not being used. However, by limiting the analysis to text matches within the HTTP and HTTPS traffic, potentially sensitive user data may have missed being observed in instances in which other protocols are used by the apps or, in cases where user data was hashed so as to obscure it.

Research Method

Our study is based on the findings presented by [START_REF] Zang | Who Knows What About Me? A Survey of Behind the Scenes Personal Data Sharing to Third Parties by Mobile Apps[END_REF] discussed in related work above. As such, our study inherited the measurement errors made in [START_REF] Zang | Who Knows What About Me? A Survey of Behind the Scenes Personal Data Sharing to Third Parties by Mobile Apps[END_REF], as mentioned above.

First, in our study, the selection of the apps was done on the basis of the number of third party domains that the apps conveyed sensitive data to. We found that in the Zang et al database, the number of third party domains associated with the apps ranged from none to 17. As such, we selected apps that conveyed sensitive data to two or more third party domains. This was based on the rationale that the greater the number of third party domains an app is linked to the higher the potential of user data dissemination. As a result, the selection yielded two non-identical sets of 15 apps on each platform (see Table 1&2). The limited sample size facilitated a detailed analysis of the apps. The analysis of this sample size was feasible taking into consideration the effort and time required for an in depth analysis. The apps were from a cross range of categories such as; social, navigation, medical, business, games, health and fitness, lifestyle etc. Hence while the sample size was relatively small, the scope of representation was relatively spread. Due to a sample size limited because it was based on a predetermined database and the selection criteria, the results are not statistically significant. However, the significance of our findings are in that they serve as a preliminary indication of trends on how the Android and iOS apps data handling practices and compliance compare. This provides indicators of further research.

Second, after determining the apps to be used in the study, we planned to analyse the apps through the following steps; (a) establishing the practical data handling practices for each of the thirty apps, (b) determining the apps' privacy commitments to users on data handling as stated in their privacy policies and, (c) establishing the extent of compliance by apps to their own privacy policies.

To establish the practical data handling practices for each of the thirty apps, we analysed the types of user data they convey to third parties in practice based on the finding of [START_REF] Zang | Who Knows What About Me? A Survey of Behind the Scenes Personal Data Sharing to Third Parties by Mobile Apps[END_REF]. In particular, 14 types of user data were found: address, birthday, email, gender, name, password, phone number, zip code, employment, friends, medical info, search, username and location. Next, we sought to establish the apps' privacy commitments to users on data handling as stated in their privacy policies. The apps' privacy policies were sourced online using the privacy policy' link provided through each app. The privacy policies were source between September to December 2015 and as such should substantially correspond to the specific version of apps that were used in [START_REF] Zang | Who Knows What About Me? A Survey of Behind the Scenes Personal Data Sharing to Third Parties by Mobile Apps[END_REF]'s study to extract the traces of sensitive data dissemination from apps. These privacy policies were uploaded into Nvivo software [START_REF]QSR: What is NVivo?[END_REF] to facilitate a qualitative analysis of their content. The process of content coding involved the review of privacy policies in order to establish a fundamental understanding of the policies. This was followed by coding using thematic analysis to identify content on data collection, use and dissemination to third parties etc., that were of particular interest to our study. The mechanism of coding and data interpretation was validated by two researchers so as to ensure substantial agreement on data interpretation and results. A study found that when six senior researchers individually coded a focus group, the results of their coding while showing major similarities in findings, also had elements of disagreement [START_REF] Armstrong | The place of inter-rater reliability in qualitative research: an empirical study[END_REF].

In the final stage, we determine the extent of compliance by apps to their own privacy policies. We systematically assessed the results from the apps' privacy commitments as stated in their policies, against their practical data handling practices involving the 14 user data types that were earlier identified. The analysis was restricted to the collected and transmitted data from the app and does not include what happens on the receiving entities. The results are presented in the next section.

Findings

Our results indicated that Android apps handle 64% of the types of the users' data examined while iOS handles 50%. Moreover, out of the types of user data gathered and disseminated by Android, 32% did not match the app privacy policies. Similarly, of the user data handled by iOS, 26% did not comply with their policies. Interestingly 14% of the iOS user data were found to be gathered and disseminated with no privacy policy available as shown in Figure 1. Most collected user data. Considering the overall figures of user data handled by the Android and iOS apps, the data attributes most collected and disseminated by Android were; address [START_REF] Hoffman | How -To Geek[END_REF], email [START_REF] Hoffman | How -To Geek[END_REF] and name [START_REF] Hoffman | How -To Geek[END_REF] i.e. these three user data attribute were collected by all the Android apps in our study since the study involved fifteen Android apps. On the other hand, iOS' highest were; location [START_REF]Ashnis: noeticforce[END_REF], email (12) and name [START_REF] Grace | Unsafe Exposure Analysis of Mobile In-App Advertisements[END_REF] i.e. none of iOS highest user attributes were collected by all the fifteen iOS attributes in the study.

Extent of compliance between policy and data dissemination. Compliance was considered as per data type. Taking into account the extent to which the apps' policies match their data handling practices, Android's most compliant users' data were; email [START_REF] Grace | Unsafe Exposure Analysis of Mobile In-App Advertisements[END_REF], name [START_REF] Grace | Unsafe Exposure Analysis of Mobile In-App Advertisements[END_REF] and location [START_REF] Liu | Smartphone privacy in mobile computing: Issues, methods and systems[END_REF]; whereas iOS had email (9), location (9) and, name and friends both (6). It appears that apart from iOS collecting friends, the other compliant user data attributes were the same for both platforms.

In contrast, considering non-compliance between the apps' policies and their data handling practices, Android's most non-compliant user data were; username (6), gender (5) and address (5) while for iOS the list comprised of; password [START_REF] Westin | Privacy and Freedom[END_REF], address (4), username and name both at (3) as shown Figures 2&3. In both iOS and Android, the similarities in non-compliance was that the username and address user data was collected and disseminated outside the privacy policy agreement, while the differences in the data handled outside the policy was that Android collected gender user data attribute while iOS collected the name user data attribute.

Further, iOS apps were found to handle users' data without privacy policies. The affected user data included; name (3), friend (2) and search (2) as shown in Figure 1.

Discussion

According to [START_REF] Krishnamurthy | On the leakage of personally identifiable information via online social networks[END_REF], there is an increase in fear regarding illicit exposure of personally identifiable information due to increasing identity theft. Personally identifiable information (PII) is sensitive and focal to privacy law [START_REF] Schwartz | PII Problem: Privacy and a New Concept of Personally Identifiable Information[END_REF]. As such, access to users' data should be aligned with the privacy policy of online social media. Laws and regulations such as the California law [START_REF] Harris | Privacy on the go[END_REF][START_REF]CalOPPA: California Online Privacy Protection Act (CalOPPA)[END_REF]; the UK the Data Protection Act 1998 [START_REF]TermsFeed: Privacy Policies are Mandatory by Law[END_REF], EU Data Protection Directive [START_REF] Directive | EUR-Lex[END_REF] etc. also require that user are provided with privacy policies before app download. However, our findings show that while the Android apps in the study were found to have policies, 14% of the iOS user data handled was from apps without policies. Similarly, [START_REF] Sunyaev | Availability and quality of mobile health app privacy policies[END_REF] study of health apps found that iOS had a 61.7% likelihood of not having privacy policies as compared to Android at 77.3%. Differences in our results may arise from the fact that we considered fewer apps [START_REF] Krishnamurthy | On the leakage of personally identifiable information via online social networks[END_REF] with more categories while [START_REF] Sunyaev | Availability and quality of mobile health app privacy policies[END_REF] considered 600 app limited to health apps. These findings highlight the fact that while the law demands for the provision of policies, major app platform are not fully complying. This is also an indicator that even when laws are enacted to protect user privacy, there is need for more effective mechanisms of enforcing these laws.

However, specifically considering compliance of apps' data handling practices their privacy policies, Android had an 18% likelihood of sharing personally identifiable information outside the limits of its policy whereas iOS' ranked slightly lower at 17 %. While our study investigated the extent of compliance between the Android and iOS apps' data dissemination against their privacy policies, a related study by [START_REF] Zang | Who Knows What About Me? A Survey of Behind the Scenes Personal Data Sharing to Third Parties by Mobile Apps[END_REF], compared Android and iOS likelihood to disseminate users' personally identifiable information in a manner not reflected by the permissions request at the apps' download, they found that Android was more likely to disseminate personal data in a way a way that breached the requested permissions.

However, taking into account the iOS apps found without privacy policies in our study, the probability of iOS sharing personally identifiable information in a non-policy compliant manner further increase from 17% to 23%, making it higher than Android (18%). Users' ability protect their personal data necessitates that they are aware of such leakages [START_REF] Krishnamurthy | On the leakage of personally identifiable information via online social networks[END_REF]. Moreover, our findings also contradict a general user perception that apps with user textual reviews are safer [START_REF] Benenson | Android and iOS users' differences concerning security and privacy[END_REF]. This is evidenced by the fact that the apps in our study had user reviews yet our findings show that some had no privacy policies. Specifically in both Android and iOS apps studied, mismatches between the policies and the data handled were most observed involving the username and address user data which are both classified as personally identified information. Further, our findings indicated that the similarities in the most collected user data in both Android and iOS was that both collected name and email user data which are both personally identified information. Nonetheless our results also showed that these two types of user data were also among the leading policy compliant user data. Our results show that in both cases of compliance and non-compliance with the apps' policies, the user data involved is personally identified information. These trends indicate the immense interest that apps have in personally identifiable information data and hence the necessity of ensuring adequate and effective user privacy preservation measures.

Our results ascertained that neither Android nor iOS apps' data handling practices fully comply with the apps' privacy policy statements. These results are restricted to the observation of data collection and transmission at app level. They do not include whatever happens on the receiving servers. In addition, the measurement errors made in [START_REF] Zang | Who Knows What About Me? A Survey of Behind the Scenes Personal Data Sharing to Third Parties by Mobile Apps[END_REF] were inherited, as mentioned above. Overall, taking into account both personal and non-personal user data analysed in the study, Android data handling practices are more compliant to policy than that of iOS with compliance figures of 68% and 40% respectively. Policies claim to limit the user data conveyed to third parties to non-personal data [START_REF] Krishnamurthy | On the leakage of personally identifiable information via online social networks[END_REF]. However, [START_REF] Montjoye | Unique in the shopping mall: On the reidentifiability of credit card metadata[END_REF] state that metadata has the potential danger of re-identification of users or sources, stressing that it is still possible to expose specific users even from non-personal data. This is underpinned [START_REF] Krishnamurthy | On the leakage of personally identifiable information via online social networks[END_REF], asserting that certain third party servers have the ability to trace and combine different pieces of user data from which a user profile can be formed. According to a study [START_REF] Malin | Betrayed by my shadow: Learning data identify via trail matching[END_REF], a combination of the zip code, gender and birthday is able to facilitate the identification of up to 87% of Americans.

Based on the finding of this study, which in tandem built on the results from [START_REF] Zang | Who Knows What About Me? A Survey of Behind the Scenes Personal Data Sharing to Third Parties by Mobile Apps[END_REF], we argue that in the preservation and protection of app user privacy a number of aspects must be considered i.e. regulation, permissions requested at apps' download, privacy policies provided and, the dissemination of user data by the apps to other apps or third parties. The relevant regulations determine the privacy requirements or best practices that must be taken into consideration by apps in order to safe guard user privacy. As such, the apps permissions, policy and dissemination of user data should be aligned to regulation. This study established that there are instances in which user data was disseminated with no policy to guide the process. For cases in which no policy is provided, users could opt not to download such apps. However, this may be unlikely [START_REF] Janger | The Gramm-Leach-Bliley Act, Information privacy and the limits of default rules[END_REF] and may depend on a users' level of privacy awareness, keenness and the personal reasons for which they require the apps service.

Further, this study shows that the dissemination of data by apps through their data handling practices does not always comply with their stated privacy policies, even in cases where potentially sensitive user data is involved. This is of concern since a study by [START_REF] Yue | User control of personal information concerning mobile-app: Notice and consent?[END_REF] found that 72% of the participants assume that the provision of a policy implies that app providers comply with the policy and necessary regulation to safeguard their privacy. We further argue that one of the criticisms of current practice is that an app may request a user to grant access to personal data which is not required for its app's functionality. This excess data may have been stated in the privacy policies, in which case it would appear as acceptable. However, it may violate the minimize principle in some regulatory frameworks [START_REF] Directive | EUR-Lex[END_REF] but not necessarily the privacy policy. There is a user expectation that regulators will protect their privacy [START_REF] Yue | User control of personal information concerning mobile-app: Notice and consent?[END_REF]. As such, this emphasizes the need for more effective mechanism of validation of apps' data handling practices against their policies.

Validation could be effected through more rigorous regulatory enforcement to monitor that apps comply to their policy. Another form of validation could take shape in form of automation of the validation process. An automated solution could function at platform level i.e. Android and iOS. At platform level, the solution could be developed first to check that and app indeed has a privacy policy before its acceptance onto the platform. Second, the automation could be used to validate compliance between apps policies and against their data dissemination practices. In a way, it would be similar to the Apple vetting process that validates that app comply with the license agreement before digital signing and uptake onto the iTunes store.

In addition to ensuring the provision of privacy policies and the validation of apps data handling practices against their policies either by regulators or through automation, several other solutions may be considered. These efforts have been geared improving policy representations in a bid to encourage or facilitate greater policy readability and user comprehension in order to encourage user reading of privacy policies so as to support informed decisions [START_REF] Earp | Privacy policy representation in web-based healthcare[END_REF] [42] [START_REF] Kununka | End User Comprehension of Privacy Policy[END_REF].

Conclusion and future work

Our results show that neither Android nor iOS apps' data handling practices meet the full requirements of their privacy policies even in cases of potentially sensitive user data. Further, instances in which iOS apps continue to disseminate user data in the absence of privacy policies were found. This is further complicated by the fact that there is no facility through which the users can confirm that the way their data is disseminated by apps matches the permissions requested by apps at download and their privacy policies. Drawing from our findings, we recommend the necessity of enhancing app platforms such that data collection is not merely checked against the app's request to use data, but that this process is enhanced by cross checking apps' data handling practices against the apps' privacy commitments to app users as stipulated within their privacy policies. As such, future research could explore ways of automating enforcement of privacy policies by drawing on privacy policy specification languages such as the Platform for Privacy Preferences (P3P) and the Enterprise Privacy Authorization Language (EPAL). This would also eliminate the transfer of data from apps that do not have privacy policies. In hindsight, a technological solution could prove the most feasible solution to this challenge through the development of a real-time graphical visual aid that depicts apps' compliance to their policies and, as well as provide automated opt-out options for users in cases of non-compliance. Taking into account considerations of the privacy requirements stipulated by regulatory frameworks such as the European General Data Protection Regulation would assist in enhancing and protecting users' privacy. In addition to building user confidence in apps' commitment to preserve user data privacy, it would also be of value to privacy regulatory bodies by automating compliance to stated privacy policies.

Figure 1 :

 1 Figure 1: A comparison of Android and iOS apps data practice verses privacy policy.

Figure 2 -Figure 3 -

 23 Figure 2 -iOS apps data practice versus privacy policy statements

Table 1 : Android apps and number of associated third parties

 1

		Third Parties
	App
	American Well	4
	Drugs.com	7
	Expedia	4
	Kayak	3
	MapQuest	5
	Priceline	4
	Glide	8
	Jobsearch	4
	Snagajob	3
	Monster Lengend	5
	Myfitnesspal	4
	Runkeeper	3
	Pinger Text Free	11
	Tango	4
	Pinrest	4

Table 2 : iOS apps and number of associated third parties

 2

		Third Parties
	App
	Fruit Ninja	4
	Piano Tiles	3
	Instagram	2
	Instasize	2
	Leafly	3
	Ovia Fertility	2
	Urgent Care	4
	MapMyRun	4
	Nike	4
	TimeShop	3
	Walgreens	5
	Groupon	3
	Inrix	2
	Local Scope	17
	Phone Tracker	2