Eleni Kosta
email: e.kosta@tilburguniversity.edu

Igor Nai Fovino

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

Marit Hansen
email: marit.hansen@privacyresearch.eu

Charles Raab
email: c.d.raab@ed.ac.uk

Ignacio Sanchez

Diane Whitehouse
email: diane.whitehouse@thecastlegateconsultancy.com

Simone Fischer Hübner

The Smart World Revolution

Keywords: smart revolution, summer school, privacy, data protection

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

An introduction to the summer school on the smart world revolution

The world is in the throes of a 'smart' revolution. From smart watches, to smart cars and from smart TV to smart robots, technological developments foster a wide range of smart applications and devices. Digital data is an essential resource for economic growth, competitiveness, innovation, job creation, and societal progress. To be exploited, data needs to flow across borders and sectors. It should be smartly aggregated and should be accessible and reusable by most stakeholders. The explosion of the phenomenon of the Internet of Things and the increasing diffusion of smart living technologies in all the layers of our society -from houses to hospitals, from cities to critical infrastructures such as energy grids -clearly demonstrates the viability and the advantages of a fully interconnected smart world. However, this vision poses concrete concerns related to the potential antagonism between the 'trend to share everything' on the one hand, and the 'citizen's right to privacy and security', on the other. Dilemmas concerning opportunities for discrimination, social profiling, and social exclusion also arise.

The European framework on data protection has been recently reformed. The European Union (EU) General Data Protection Regulation (GDPR) 1 replaces the 1995 Data Protection Directive 2 and becomes applicable as of May 2018, providing an overarching legislative framework that responds to the concerns of processing personal data. Parallel to the adoption of the GDPR, a new Directive was adopted to protect personal data processed for the purpose of criminal law enforcement (LED) 3 . The 2002 e-Privacy Directive 4 regulates issues relating to privacy and data protection in relation to electronic communications services offered over public communications networks. In January 2017, the European Commission published a proposal 5 for the revision of that Directive as well. The Commission proposed the replacement of the Directive by a Regulation that may eventually provide an instrument to enforce not only the privacy, but also, to some extent, the security of the upper layers of the telecommunication services relevant for implementing a smartly interconnected world. One of the novelties of the proposed e-Privacy Regulation is the extension of its scope to include new functionally equivalent electronic communications services offered by Over The Top (OTT) players i.e., with no involvement of multi-system operators. The goal of the expansion of the scope of the Regulation to OTTs is to ensure respect for the principle of confidentiality and the protection of fundamental rights in those types of services.6 While these legislative instruments define the principles to be respected and enforced, not a lot has been said about the way in which these principles should be deployed technically in different industrial and societal sectors. Technological advances such as the use of open data, big data, blockchain and sensor development in the Internet of Everything are rapidly changing the societal landscape. Questions arise about who holds what data, and where and how that data may be used. These advances challenge the way in which privacy and data protection should be handled, because current national regulatory mechanisms were not devised with these new technologies and possibilities in mind. What is also clear, from discussions in the general press, media and social media, is that there are also huge societal, social, and ethical concerns with regard to the implications of these emerging technologies both in theory and in their practical deployment.

Here, indeed, lies a major scientific and social challenge: how to guarantee, in a homogeneous way, the preservation of privacy and other human rights in a completely heterogeneous and cross-sectoral world, without impairing the potentialities of the new smart technologies such as the Internet of Things and big data. Such questions, as well as many other current and general research issues surrounding privacy and identity management, were addressed by the 2017 IFIP Summer School on Privacy and Identity Management.

The Summer School welcomed contributions from PhD students from various disciplines (e.g., computer science, informatics, economics, ethics, law, psychology, sociology, history, political and other social sciences, surveillance studies, business and public management). These contributions were supplemented by invited talks from experts in the field, as well as tutorials and workshops. This volume reflects the outcome of the exciting work presented at the summer school and mirrors the sparkling interdisciplinary debates that took place during and around the event. The submissions are divided into six categories, each dealing with a concrete aspect of the 'smart revolution': privacy engineering, privacy in the era of smart revolution, improving privacy and security in the era of smart environments, safeguarding personal data and mitigating risks, assistive robots, and finally, mobility and privacy.

Privacy engineering

Privacy engineering is a key topic that aims at the development of methodologies, tools and techniques that contribute to ensuring privacy in engineered systems. Given the importance of privacy engineering in the era of 'smart revolution', the Summer School organisers invited three groups of distinguished researchers to cover various aspects of this topic. Privacy and data protection by design are important principles embraced by the privacy and data protection community: they were recently captured in the content of the GDPR. The notion of "by design" implies that the privacy and data protection dimension should be considered at the inception of new projects and taken into account all the way during the projects' design and development.

In Chapter 2, Del Alamo, Martín and Caiza, in their invited paper, provide an introduction to the emerging field of privacy engineering, which aims to bridge the gaps among the legal, the academic and the engineering dimensions with the goal of providing engineers with tools and methods to address data protection and privacy during the software development process. In this work, the authors describe a conceptual metamodel that can be used to organise and structure knowledge in this domain in a way that can be more easily used and integrated into engineering practice. They elaborate on privacy design patterns as means to gather systematic reusable knowledge.

The topic of data protection by design is also at the core of the chapter authored by Rommetveit, Tanas and van Dijk. This invited paper presents results from recent empirical investigations on privacy by design and privacy engineering (the H2020 CANDID project), in which social science methods such as written consultations, questionnaires, interviews and focus groups were used to research the existing approaches, perceptions, imaginations and expectations in regard to privacy engineering. Tensions and frictions discovered during these investigations are described in the chapter, such as lessons learnt relating to the limits of privacy by design that should not be passedthe impossibility of "law becoming code" in the strict sense of the word.

In Chapter 4, Marx, Sy, Burkert, and Federrath focus on the heavily debated issue of ensuring anonymity in the online world. Their contribution starts with an overview of relevant terminology particularly in the field of computer science, points to data protection regulation on the European level, and shows their work on formalising anonymity. Furthermore, the authors give an overview of different techniques and solutions providing anonymity on the application level as well as on the network level, protecting against adversaries of different strengths. They conclude that the protection of users' privacy on the Internet becomes increasingly difficult and discuss risks such as those involved in fingerprinting as well as storage-based and behaviour-based tracking. Finally, they identify open research questions, in particular with respect to practical and usable anonymity on the Internet.

Privacy in the era of the smart revolution

Issues revolving around conceptualisation of privacy and principles relating to privacy protection could not but be in the focus of numerous contributions to the Summer School. The increased popularity of interconnected devices puts privacy and data protection at the centre of the cyber-arena even more than in the past. However, the debate is not only around technologies, but also around their users and how they perceive privacy.

Kitkowska, Wastlund, Meyer and Martucci performed an empirical investigation on the perception of privacy harm. In their work they identify differences in privacy concerns related to information disclosure, protection behaviour and demographics. Additionally, their results suggest that there are some general tendencies in privacy concerns. Their findings show that people create simplified models of privacy harms, such as worries about security, unlawful use of data, disclosure or exposure.

Three co-authors (Patrignani, Whitehouse and Gemo) investigate the changes that have taken place in the concept of privacy, due to both societal and technological advances, based on a 1999 consideration that one should forget about data privacy. This topic was discussed during one of the workshops held at the Summer School, through the prism of disciplines such as ethics, philosophy, and education. The goal of the workshop was to stimulate reflection and discussion among attendees by assessing a broad set of questions. Examples follow: Is privacy still a human right? What are the implications of data collection and the cloud for privacy? What will become of the concept of privacy in the future? What kinds of education will be needed in the future to inform children and young people particularly about notions of privacy and future technological developments? Each of four main questions/topics are handled, analysed and discussed in a separate section of Chapter 6. The co-authors then lay out tentative conclusions, emerging both from and after the workshop.

In their invited paper, Sabelli and Tallacchini approach the principle of fairness as a core principle in privacy protection in the era of algorithms and big data. The authors provide an overview of the historical evolutions of privacy legislation in the United States (US) and the European Union (EU). They further discuss how the legal and ethical debates on the digital world in the two continents have significantly opened up to include new dimensions other than privacy, particularly in connection with machine learning algorithms and big data. While acknowledging that privacy and data protection remain essential interpretive constructs in the information and communication technology (ICT) domain, they can no longer be seen as capable of capturing all relevant normative issues in the digital space. Issues of discrimination, equal opportunity, fairness and, more broadly, models of justice, are gradually entering the picture, requiring novel approaches. The authors offer concrete examples of the inadequacy of privacy and data protection to cover new normative concerns related to big data and machine learning, such as the broadly known anti-spam filters. According to the authors, however, attempts to grant algorithmic fairness represent just the first step in solving new digital normative issues. The wider question about what models of justice people are willing to take into account and apply still needs to be addressed.

The topic of the next chapter is closely related to the general question about fairness and the role of individuals. Consent -what it means, how it can be implemented, what part it plays in legitimising data processing -is a central topic and focus of debate in the communities of scholars, regulators and practitioners of data protection. Whether it is possible for consent to be 'freely given, specific, informed and unambiguous' (GDPR, Art. 11(4)) in all the contexts in which it is sought engages many complex forms of behaviour in terms of law, communication processes, cognitive processes and power relationships that provide the conditions that must be satisfied if consent is to be more than a rhetorical or ideological gesture in the idiom of informational self-determination. Bergemann's discussion is timely and contributes to an understanding of consent by examining a large body of existing literature and exploring what he calls the 'consent paradox': 'How can we account for the prominent position of consent despite critique?'. This is set within the long-running German debate about consent, which the author analyses using the tools of discourse analysis rather than those of philosophy and law. He casts light on how the 'paradox' is sustained through the very critique of consent, and not despite it. The critique emphasises the larger social issues and conflicts that come to the fore in and around the idea and practice of consent across many fields of data processing. His conclusion points to a rethinking of data protection in terms of a more comprehensive critique of power.

The notion of the Internet of Things has gained in prominence, reaching public attention over the past decade. Devices are now on the market and are being increasingly used. In Chapter 9, Railean and Reinhardt conduct a questionnaire survey on Internet of Things products: their acquisition, use and eventual discarding/de-commissioning, and the implications that these three activities have for privacy. Implicit in the authors' investigation of the devices -many of which may be embedded or hidden -is a lifecycle approach. This chapter raises concerns with regard to 'digital literacy' or, perhaps, the 'privacy literacy', of people in contemporary society, especially users who -for various reasons -may be relatively vulnerable. The end focus of the paper is, however, on a set of nine recommendations made to the vendors of Internet of Things devices, which may individually or collectively improve the companies' privacy practices. Replication of the work by the same (or different) researchers in other settings and/or on the larger scale than the 110 completed responses achieved by this research may be encouraged by the fact that the 30+ survey questions are laid out in an annex to the paper.

Improving privacy and security in the era of smart environments

The third set of contributions aimed at proposing solutions or approaches that will contribute to the improvement of privacy and security.

Chapter 10 summarises the workshop that two H2020 projects jointly held at the Summer School: the CREDENTIAL project and the PRISMACLOUD project. Both projects employ cryptographic primitives for cloud services that aim at improving security and privacy features. The workshop organisers presented several concepts and pilots of privacy-preserving solutions that enable storing, sharing, and processing of potentially sensitive data in untrusted clouds environments. The vivid discussion that followed encompassed junior and senior researchers from multiple disciplines and resulted in some new aspects that may help the further evolution of the concepts and implementations as well as ways to demonstrate their advantages.

Sakpere and Kayem address, in Chapter 11, the need for re-designing existing applications under privacy-based-service-oriented principles. In particular, in their paper, they present a solution for anonymising streaming crime data using k-anonymity, ldiversity and t-closeness approaches.

In line with some of the considerations that motivated the previous work, Shulman, with a completely different approach, argues that to address privacy-related decisions it is necessary to consider aspects of human cognition, employing, for instance, methods used in Human-Computer Interaction (HCI) and Information Science research. In his contribution, Shulman analyses findings and contributions of existing privacy decision-making research and suggests filling gaps in current understanding by applying a cognitive architecture framework to model privacy decision-making.

Safeguarding personal data and mitigating risks

Closely linked to the previous session of the school are contributions that focus on the safeguarding of personal data and the mitigation of risk, especially in view of the new principles and rights introduced in the GDPR.

In Chapter 13, Bieker, Martin, Friedewald and Hansen report on their interactive workshop that dealt with Data Protection Impact Assessment (DPIA), which plays an important part in the GDPR as an instrument for assessing and mitigating data processing's potential risk to rights and freedoms. DPIA builds on decades of practice that was shaped around Privacy Impact Assessment (PIA), of which there have been many varieties and levels of quality around the world. The GDPR, however, is unclear about what is expected in a DPIA, and how to perform it, although it is mandatory in many cases. The workshop aimed to enable participants to become more familiar with DPIA, how it can be carried out, and what issues are likely to arise in this process. It did this through expounding a diagrammatic Standard Data Protection Model, explaining the inventory of data protection goals derived from legal requirements, and involving the workshop attendees in examining two case studies in terms of risks to rights and freedoms: smart surveillance in train stations, and emotional coding for in-store advertising. The authors conclude that more research and refinement is needed to improve the ability to implement DPIA in complying with the GDPR, and that a multidisciplinary perspective is required to identify and mitigate risks.

In Chapter 14, Raschke, Küpper, Drozd and Kirrane focus on designing a GDPRcompliant and Usable Privacy Dashboard. The chapter outlines the design, implementation and first expert user evaluation of a Privacy Dashboard that was developed in the scope of the EU H2020 project, SPECIAL, for enabling data subjects to exercise their data subject rights in compliance with the GDPR. A specific focus, in contrast to previous related work on privacy dashboards, is on consent review and withdrawal.

Neisse, Steri and Nai Fovino provided an invited contribution in the form of an extended abstract on blockchain-based identity management and data usage control. The authors discuss the approach for a blockchain-based solution based on smart contracts, which helps to enforce the transparency and accountability principles of the GDPR. In particular, their solution allows end users to track how their data were processed by data controllers and data processors and whether the processing of their data is compliant with their provided consent. Controllers and processors can in turn prove that they have rightfully obtained consent and are not violating data protection obligations. Three different solutions for provenance and accountability tracking models using blockchains are analysed and compared in regard to privacy, performance and scalability.

The invited extended abstract of Ferrari follows, focusing on Identification Services for Online Social Networks (OSNs). Given that digital identities are a key element of OSNs, their users become more reliant on online identities with often no means of knowing who really is behind an online profile. The problem of fake accounts and identity-related attacks in OSNs has attracted considerable interest from the research community. In her short paper, Ferrari provides an overview of the Sybil attack problem, briefly discussing the research proposals aimed at empowering users with tools that can help them to identify the validity of the online accounts with which they interact.

Chapter 17 summarises a workshop given at the Summer School on a design and reporting toolkit for experimental HCI research related to privacy and cyber security that was presented and practically applied to exemplary publications. Coopamootoo and Gross present a set of 'completeness indicators' for the quality of experimental research that can support researchers in the design phase of a user study and provide a structure for writing and reviewing research papers in that area.

Assistive robots

A book on today's 'smart revolution' would be incomplete without contributions on robots and their ethical implications.

Based in Germany, Heuer, Schiering and Gerndt have begun to explore some of the ethical concerns, especially around privacy, implied by the use of socially assistive robots. Their almost 60-item literature search -a meta-study -has permitted them to structure the problem area. Not only did the authors categorise socially assistive robots into four types -ranging from the more functional to the most human-like -but they also explored how three categories of users -children, families, and older adultsmight experience privacy concerns. Ultimately, their research shows that relatively little attention has been paid to date to the ethics of socially assistive robots. This conclusion appears especially surprising when one considers the vulnerability of the parties involved. The ethics of robot design and robot use is clearly a domain to watch. One should anticipate more in-depth investigations of the ethical, social, and societal implications of robot use in a range of fields, especially over the life-course of human beings.

In Chapter 19, De Conca presents a theoretical model called the Aggregated Privateness Model, inspired by the structure of snowflakes, to explain a change in the 'positioning' of the home with regard to its role as place of expression and protection of the private sphere. The change is connected to the presence of robotic intelligent assistants such as Google Home and Amazon Echo inside the home, and in particular with the constant fluxes transferring information in and out of the private sphere. The data collected and transferred by such devices creates several detailed profiles and predictive models that overlap with the individuals inside the home, creating an entire informational ecosystem around them while maintaining certain aspects of 'privateness' and seclusion. This projects the home and the private spheres of individuals into a dense informational structure, a hive of data (described by De Conca's model as an interaction of clusters of nodes and aggregates of privateness) whose connections spread in different directions. In this way, De Conca's model sheds light on a more collective dimension of privacy, a context in which mathematical rules gain normativity. The model highlights how the behaviour of individuals can influence the other private spheres in the clusters and the aggregation itself due to a network effect, and how Diffused Network Liability could help compensate for such influences without becoming practically impossible.

Mobility and privacy

It has been more than 20 years since mobile phones became an integral part of the everyday life of individuals, a phenomenon that intensified with the introduction and exponential growth of smartphone devices. This collection of chapters on mobility and privacy contains texts that are focused on mobile phones and the vast capabilities they offer.

Kununka, Mehandjiev and Sampaio present the results of a study in which they analyse the privacy policies of a dataset of Android and IOS apps to determine to what extent these policies reflect the actual behaviour of the apps in terms of collection and flows of personal data to third parties. Their results show that there are a substantial numbers of apps, both in Android and IOS, that collect, manage and disseminate personal data in a way that does not match the description and requirements provided in their privacy policies. These results highlight the need to put in place mechanisms to ensure the enforcement of privacy policies and secure their alignment with the actual behaviour of the apps. The authors suggest that privacy policy specification languages could be used for the automatic enforcement of privacy policies, facilitating the development of technological solutions that could assist in the monitoring of the compliance of apps with their policies and the provision of an opt-out for users in cases of noncompliance.

In their paper, Harborth and Pape examine the privacy-related behaviour of players of Pokémon Go -a location-based augmented reality game available on mobile phones -in Germany. The two researchers have conducted an online two-part survey to study both the attitudes and the behaviour of players. Their focus is chiefly around the privacy paradox. They examine the extent to which players express their concerns about privacy and whether they then behave in a privacy-preserving way. Three highlights of the paper are: the examination of the differences in attitude/behaviour dependent on the age and the gender of the Pokémon Go players; the wealth of the literature explored; and the availability of the survey questions in an annex. As a result, other researchers may be tempted to replicate the study in different cultural settings and with the same (or extended) augmented reality applications. Will the dilemmas surrounding the gap between expressed concern and actual behaviour remain? And will utilitarian trade-offs continue?

Data processing in the health sector is based on sensitive data. It is of the utmost importance that privacy risks are detected and mitigated. This is the starting point for the contribution from Gabel, Schiering, Müller and Ertas. The authors focus on mobile health applications and analyse the scenario of neuropsychological training after brain injuries. Employing privacy protection goals and privacy design strategies, they model privacy requirements such as the pseudonymity of patients, data minimisation and transparency. Finally, the authors suggest technical and organisational measures to implement those requirements.

Near Field Communication (NFC) is being used for payments. In the contribution from Kumar, Bechinie and Tscheligi, the authors investigate the gaps between user perception and reality, in particular concerning privacy and security aspects. They conducted a study that showed that users have different mental models about NFC. The authors propose to modify NFC payment systems in such a way that users can gain experience not only in their usage, but also better understand privacy and security concepts.

In Chapter 24, digital identities are discussed as a key element of the future digital society. Today there is already a broad range of existing electronic identity (eID) systems, which provide methods to sign documents or authenticate online services. Quite often, however, these identities lack appropriate techniques so that they can be used as regular identity cards to digitally authenticate an eID holder to a physical person in the real world. Starting from this assumption, Hölzl, Roland and Mayrhofer explore a new way of implementing mobile eID taking into consideration this requirement in a privacy-preserving fashion.

Outlook to the future

The annual IFIP Summer Schools offer a wonderful platform for PhD students and experienced researchers alike, to present their work and to benefit from the exchange and cross-fertilisation of ideas during an intensive week of lectures, presentations, tutorials and workshops. The contributions to the 2017 IFIP Summer School that were presented either as PhD papers, invited talks or workshops, all cover different aspects of privacy and security in the era of the smart revolution: from privacy engineering to risk mitigation and from assistive robots to mobile applications. The 2017 IFIP Summer School offered an exciting journey through the challenges that are raised by recent technological developments and the rethinking and novel conceptualisations of traditional issues in the scholarly debates on privacy and identity management. It is our aspiration that research in this field will continue, and that answers to some of the questions that were raised during the Summer School and that are included in chapters of this volume will be answered in the coming years. And -why not? -maybe in one of the following IFIP Summer Schools.

  European Parliament and the Council of the European Union, Directive (EU) 2016/680 of 27 April 2016 on the protection of natural persons with regard to the processing of personal data by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties, and on the free movement of such data, and repealing Council Framework Decision 2008/977/JHA [2016] OJ L 119/89 (4.5.2016). 4 European Parliament and the Council of the European Union, Directive 2002/58/EC of 12 July 2002 concerning the processing of personal data and the protection of privacy in the electronic

	communications sector (Directive on privacy and electronic communications) [2002] OJ
	L201/37 (31.07.2002).
	5 European Commission, Proposal for a regulation of the European Parliament and of the Council
	concerning the respect for private life and the protection of personal data in electronic com-
	munications and repealing Directive 2002/58/EC (Regulation on Privacy and Electronic Com-
	munications) {COM(2017) 10 final}.

1 European Parliament and the Council of the European Union, Regulation (EU) 2016/679 of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance) [2016] OJ L 119/1 (4.5.2016). 2 European Parliament and the Council of the European Union, Directive 95/46/EC of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data [1995] OJ L281/31 (23.11.1995). 3

European Commission, Proposal for a Regulation of the European Parliament and of the Council concerning the respect for private life and the protection of personal data in electronic communications and repealing Directive 2002/58/EC (Regulation on Privacy and Electronic Communications), Brussels, 10.1.2017 COM(2017) 10 final, p. 4.


Agnieszka Kitkowska
email: agnieszka.kitkowska@kau.se

Erik Wästlund

Joachim Meyer

Leonardo A Martucci

Is It Harmful? Re-examining Privacy Concerns

Keywords: Privacy, Human Factors, Attitudes, Decision Making

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

Introduction

The widespread Internet availability and access to various devices, from PCs, through mobile to smart devices, enabled the establishment of an ecosystem of interconnected applications. People adapt these technologies and feed them with a large amount of data. Such applications assist people with performing most of their daily activities, including socializing, healthcare, financial transactions, work and more. People voluntarily, and sometimes unknowingly contribute data to Internet-based applications, and that may expose them to privacy risks, violations, and harms.

Due to the increasing amount of security breaches, digital privacy became a subject of public debate. The news about data leakages and their potential effects frequently appear in media, informing the audience about the potential privacy risks. Since privacy violations are in the center of interest, governments and policymakers introduced legal guidelines and regulations aiming to protect personal data, such as the General Data Protection Regulation (GDPR) in Europe [START_REF] Regulation | Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46[END_REF] or FTC requirements in USA [START_REF] Solove | The FTC and the New Common Law of Privacy[END_REF]. Simultaneously, the academic research resulted in multiple studies about online privacy, demonstrating that people are concerned about their data, nevertheless, they trade them for potential benefits arising from applications [START_REF] Acquisti | The Economics of Privacy[END_REF][START_REF] Taddicken | The 'Privacy Paradox' in the Social Web: The Impact of Privacy Concerns, Individual Characteristics, and the Perceived Social Relevance on Different Forms of Self-Disclosure[END_REF][START_REF] Dinev | Internet privacy concerns and their antecedents -measurement validity and a regression model[END_REF]. Despite the efforts of researchers and policymakers, as well as increased privacy awareness raised in media, people's attitudes and behaviors remain unchanged. Regardless of their concerns, people provide personal information to online companies to use their services, ensure social interactions, improve well-being and more.

The aim of this study is to investigate privacy perceptions and to re-examine some of the privacy behaviors. The primary contribution of this research is a novel instrument to measure privacy attitudes, Privacy Harms Concerns (PHC) scale. Following the recommendation of the past research [START_REF] Kokolakis | Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon[END_REF], we used privacy harms identified by Daniel Solove as a foundation for the scale's development [START_REF] Solove | A taxonomy of privacy[END_REF] (Table 1). The goal of this research was to identify how people perceive privacy concerns relevant to harms (to ensure consistency labeled privacy concerns throughout the article). The results confirmed that people, in spite of their diversity, tend to have rather comprehensive and simplified view of privacy concerns, perceiving their severity and importance in a similar manner. Regardless of this general tendencies, we identified differences in privacy perceptions, information disclosure, and protection behaviors. Additionally, the findings demonstrate a potential for demographic differences in privacy concerns. Overall, the results contribute to further understanding of people's privacy attitudes and behaviors. According to Westin, privacy concern is the intention to protect personal information from others [START_REF] Buchanan | Development of measures of online privacy concern and protection for use on the internet[END_REF]. Thus it carries a negative weight and should result in preventive or protective actions. As defined by Campbell, the information privacy concern is a subjective notation concentrated around the input, use and control of data [START_REF] Malhotra | Internet users' information privacy concerns (iuipc): The construct, the scale, and a causal model[END_REF]. Therefore, the information concern is related to the flow of data between the user and involved data processors. The online privacy research recognized various antecedents of privacy concerns such as trust, risk perception, previous privacy experience, privacy awareness, personality traits and demographic differences [START_REF] Lutz | Privacy concerns and online behavior -Not so paradoxical after all? Multinationale Unternehmen und Institutionen im Wandel Herausforderungen für Wirtschaft[END_REF][START_REF] Slyke | Concern for Information Privacy and Online Consumer Purchasing[END_REF][START_REF] Bergström | Online privacy concerns: A broad approach to understanding the concerns of different groups for different uses[END_REF][START_REF] Stutzman | Factors mediating disclosure in social network sites[END_REF][START_REF] Harbach | Using personal examples to improve risk communication for security & privacy decisions[END_REF][START_REF] Kehr | Rethinking privacy decisions: pre-existing attitudes, pre-existing emotional states, and a situational privacy calculus[END_REF]. Some of this research investigated the influence of concerns on privacy behaviors but the results are inconsistent. Some studies show that despite concerns, people disclose information, however, it is a natural consequence of being a part of community [START_REF] Lutz | Privacy concerns and online behavior -Not so paradoxical after all? Multinationale Unternehmen und Institutionen im Wandel Herausforderungen für Wirtschaft[END_REF]. On the other hand, there is a large volume of research illustrating, that regardless of privacy concerns, people tend to share their information, and their decisions are based on cost and benefit trade-off [START_REF] Roback | Privacy risk versus socialness in the decision to use mobile location-based applications[END_REF][START_REF] Acquisti | Privacy attitudes and privacy behavior[END_REF][START_REF] Fagan | A Study of What Motivates Users to (Not) Follow Computer Security Advice[END_REF]. This so-called privacy paradox is frequently explained by factors such as information asymmetry [START_REF] Acquisti | Privacy and Human Behavior in the Age of Information[END_REF][START_REF] Acquisti | Privacy attitudes and privacy behavior[END_REF][START_REF] Adjerid | Sleights of Privacy : Framing , Disclosures , and the Limits of Transparency[END_REF] or psychological biases and heuristics [START_REF] Brandimarte | Misplaced Confidences: Privacy and the Control Paradox[END_REF][START_REF] Gambino | User disbelief in privacy paradox: Heuristics that determine disclosure[END_REF][START_REF] Krasnova | It Won't Happen To Me!": Self-Disclosure in Online Social Networks[END_REF][START_REF] Camp | Mental models of privacy and security[END_REF][START_REF] Kehr | Privacy Paradox Revised: Pre-Existing Attitudes, Psychological Ownership, and Actual Disclosure[END_REF].

To the best of our knowledge, privacy concerns have not been investigated from the perspective of privacy harms. Similarly to the notion of privacy itself, there is no clear definition of privacy harm. However, scholars from legislative sector tried to provide a coherent explanation of the term. For instance, Solove identified a privacy problem as a result of harm, claiming that harms do not have to be physical or emotional, they can occur by chilling socially beneficial behavior (for example, free speech and association) or by leading to power imbalances that adversely affect social structure (for example, excessive executive power) [START_REF] Solove | I've got nothing to hide and other misunderstandings of privacy[END_REF]. Similarly, Calo defines harm as a conceptualized negative consequence of privacy violation [START_REF] Calo | The boundaries of privacy harm[END_REF]. Nevertheless, the most comprehensive definition of privacy harms we found was provided by researchers investigating smart grids privacy, De & Métayer. They defined harm as the negative impact on a data subject, or a group of data subjects, or the society as a whole, from the standpoint of physical, mental, or financial well-being or reputation, dignity, freedom, acceptance in society, self-actualization, domestic life, freedom of expression, or any fundamental right, resulting from one or more feared events [START_REF] De | Privacy harm analysis: a case study on smart grids[END_REF]. In this research, we follow this definition and consider harms as a multidimensional notion.

The previous research resulted in multiple scales measuring privacy concerns. Such measuring scales are constructed in various ways, for example by asking people directly about their concerns, treating concerns as latent variables or as moderators [START_REF] Preibusch | Guide to measuring privacy concern: Review of survey and observational instruments[END_REF]. For instance, Smith et al. [START_REF] Smith | Information privacy: measuring individuals' concerns about organizational practices[END_REF] developed Concerns for Information Privacy (CFIP) scale aiming to explore the concerns' structure. The study identified four dimensions of privacy concerns: improper access, unauthorized secondary use, error, and collection. Malhotra et al. developed Internet Users Information Privacy Concern (IUIPC) scale identifying three dimensions: collection, control, and awareness of privacy practices [START_REF] Malhotra | Internet users' information privacy concerns (iuipc): The construct, the scale, and a causal model[END_REF]. According to their research consumers perceive as the most important awareness and control over their data stored by online companies. The IUIPC scale can be applied to privacy research in various contexts. Regardless of the coherent nature of this scale, it seems to be an organization-and consumer-oriented, as authors put it, IUIPC is representation of online consumers' concerns for information privacy. Buchanan et al. developed another privacy concerns scale, measuring individual privacy issues, asking directly about concerns, for instance regarding personality theft, access to medical records etc [START_REF] Buchanan | Development of measures of online privacy concern and protection for use on the internet[END_REF].

Considering the definitions of privacy harms and the past research, we want to improve understanding of attitudes and re-examine dimensionality of privacy concerns. Hence, the first research question:

RQ1 How do people perceive privacy harms concerns? RQ1.1 What are the main dimensions of privacy concerns?

RQ1.2 Are some concerns perceived as more severe than others?

Privacy behaviors

In order to cross-validate findings of privacy concerns, some research examined their relations with other attitudinal or behavioral factors, such as information disclosure or protection behavior.

According to the research, the information disclosure behavior varies, depending on psychological states [START_REF] Raij | Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment[END_REF], risk perceptions [START_REF] Coventry | Perceptions and actions: Combining privacy and risk perceptions to better understand user behaviour[END_REF][START_REF] Trepte | Risky behaviors: How online experiences influence privacy behaviors[END_REF], trust and more. Several studies explored the relationship between privacy concerns and information disclosure. For example, research showed significant effects of privacy concerns on information disclosure, influenced by psychological biases, such as optimism bias, over-disclosure and others [START_REF] Krasnova | It Won't Happen To Me!": Self-Disclosure in Online Social Networks[END_REF][START_REF] Stutzman | Factors mediating disclosure in social network sites[END_REF][START_REF] Preibusch | The privacy economics of voluntary over-disclosure in web forms[END_REF]. Similarly, the researchers found evidence of irrational behavior, when people tend to disclose data knowingly about the potential risks [START_REF] Egelman | my profile is my password, verify me!": the privacy/convenience tradeoff of facebook connect[END_REF].

In this research, we will not examine factors influencing privacy concerns or the direction of the relationship between attitude and behavior. Instead, we focus on a variance of privacy concerns among people who disclose (or not) sensitive or non-sensitive information. Hence, our next research question:

RQ2 Is there a relationship between privacy concerns and privacy behavior? RQ2.1 Do privacy concerns vary among people disclosing and not-disclosing non-sensitive information?

RQ2.2 Do privacy concerns vary among people disclosing and not-disclosing sensitive information?

The past privacy research identified control as an important factor influencing privacy behaviors [START_REF] Fogel | Internet social network communities: Risk taking, trust, and privacy concerns[END_REF][START_REF] Adjerid | Sleights of Privacy : Framing , Disclosures , and the Limits of Transparency[END_REF]. To achieve control over online information disclosure people apply different protection measures. Some use technical protections, such as anti-malware or anti-virus software, add blockers, or other privacy enhancing technologies. Others may be more careful about their physical privacy (hiding PIN, shredding documentation), limit information provided to social networks (such as reduction of the posts' audience, limited profile visibilities etc.), decrease number of online profiles or even entirely resign from the online presence. The relationship between privacy concerns and protection is unclear. There is some research claiming, that such relationship exists, however, the correlations are low and people less concerned about privacy use more of protective measures [START_REF] Acquisti | Privacy and Rationality in Individual Decision Making. Challenges in Privacy Decision Making. The survey[END_REF][START_REF] Park | Affect, cognition and reward: Predictors of privacy protection online[END_REF].

Considering the past research demonstrating that the relationship between concerns and behavior exists, we ask following questions: RQ2.3 Is there a relationship between people's privacy concerns and general privacy caution? RQ2. [START_REF] Acquisti | The Economics of Privacy[END_REF] Is there a relationship between people's privacy concerns and technical privacy protection?

Demographics

To assess individual differences in privacy concerns some of the researchers used demographics, such as geographic/cultural differences, age, education or gender [START_REF] Reed | Differences in online self-disclosure behavior across national cultures[END_REF][START_REF] Cho | A multinational study on online privacy: global concerns and local responses[END_REF]. However, the results of studies investigating demographic dependencies are inconclusive. For instance, there are studies claiming that gender impacts privacy perceptions and females are more concerned about their data than males. However, some of these findings show that the impact of gender on privacy attitudes and behaviors is indirect or insignificant [START_REF] Hoy | Gender Differences in Privacy-Related Measures for Young Adult Facebook Users[END_REF][START_REF] Sheehan | An investigation of gender differences in on-line privacy concerns and resultant behaviors[END_REF][START_REF] Park | Do men and women differ in privacy? Gendered privacy and (in)equality in the Internet[END_REF]. Regarding the age, there seems to be a general tendency that older generations are more concerned about their privacy than the younger ones [START_REF] Steijn | Why concern regarding privacy differs: The influence of age and (non-) participation on facebook[END_REF][START_REF] Miltgen | Cultural and generational influences on privacy concerns: a qualitative study in seven European countries[END_REF]. Nevertheless, it does not mean that younger people ignore it. In contrary, the research demonstrates that younger people use technical protection measures to better manage their privacy [START_REF] Marwick | Networked privacy: How teenagers negotiate context in social media[END_REF].

The previous research associated privacy concerns with geographic/cultural background [START_REF] Sun Lim | A multinational study on online privacy: global concerns and local responses[END_REF][START_REF] Bellman | International differences in information privacy concerns: A global survey of consumers[END_REF]. The geographic divide was confirmed in the qualitative study of seven European countries, identifying main privacy concerns influencing information disclosure and a variety of privacy fears among different nationalities [START_REF] Miltgen | Cultural and generational influences on privacy concerns: a qualitative study in seven European countries[END_REF]. Similarly, other studies showed differences among respondents from North America and Europe [START_REF] Sheth | Us and Them: A Study of Privacy Requirements Across North America, Asia, and Europe[END_REF], and France and Hong Kong [START_REF] Ho | From privacy concern to uses of social network sites: A cultural comparison via user survey[END_REF]. Such differences were accredited to cultural dimensions, for instance, assertiveness or gender egalitarianism [START_REF] Reed | Differences in online self-disclosure behavior across national cultures[END_REF][START_REF] Sun | Location information disclosure in location-based social network services: Privacy calculus, benefit structure, and gender differences[END_REF].

Considering the previous research's findings, we aim to examine whether there are any significant demographic differences in privacy concerns and behaviors among the participants of our study. Hence, our last research question:

RQ3 Do privacy concerns differ depending on the demographic background?

Method

The online survey was created to answer the research questions. It contained 80 questions, divided into thematic sections, such as participants' demographics, opinions related to data collection and processing, security, identity, and personal questions. To measure the responses, we used mixed design, including questions collecting responses on the scale ranging from 0 to 100 (strongly disagree/strongly agree; never/always) and multiple choice questions.

Before participating in the survey, respondents were presented with informed consent, explaining what type of information will be requested during the survey, what is the study purpose and who should be contacted in case of any questions. Each participant had to agree to the informed consent and confirm that he/she is over 18 years old.

Instrument

The online survey consisted of three major sections: the new scale to measure privacy concerns, and two scales acquired from the past research, measuring privacy behaviors. Due to the thematic division of the survey and to ensure the instrument's consistency, some of the questions from the PHC were mixed with questions from the scale measuring protection behavior.

To create the new scale, we applied the privacy harms framework defined by Solove [START_REF] Solove | A taxonomy of privacy[END_REF]. We developed the 48 items scale derived from Solove's 16 privacy harms. Solove categorized 16 privacy harms into four groups, which are presented in Table 1. Solove's work addresses privacy harms from the legal perspective, however, in the past it was used in the information privacy research [START_REF] Knijnenburg | Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems[END_REF]. Additionally, we believe that privacy harms may be recognizable and meaningful, since the framework origins from court cases and real-life examples. Originally we aimed to measure each individual privacy harm, hence we used three items for each of them. The instrument collected continuous data, scores ranging from 0 to 100 (strongly disagree/strongly agree). After all data were collected, some of the items were modified, to ensure scores' consistency.

The scale measuring information disclosure was acquired from Joinson et al. [START_REF] Joinson | Measuring self-disclosure online: Blurring and non-response to sensitive items in web-based surveys[END_REF]. It consisted of 11 items, asking respondents questions of personal nature.

To ensure consistency, the information disclosure scale was modified and did not include two questions requiring respondents to type answers in the text boxes. The scale aimed to measure disclosure of sensitive and non-sensitive information. The sensitive items were measured by asking intimate questions, such as 'How many different sexual partners have you had?'. The non-sensitive items contained less invasive questions, for instance 'Are you right or left handed?'. The disclosure level was measured by providing respondents with option 'I prefer not to say', which if chosen was coded as 1 (don't disclose). All other responses were coded 0. In a result participants who do not disclose scored 5 per sensitive and 4 per non-sensitive items. All other participants were treated as disclose group. This resulted in division of respondents to two groups: disclosing sensitive information (N = 273) and non-disclosing sensitive information (N = 109), and disclosing non-sensitive (N = 325) and non-disclosing non-sensitive information (N = 57).

The second scale acquired from the previous research aimed to measure protection behavior [START_REF] Buchanan | Development of measures of online privacy concern and protection for use on the internet[END_REF]. It consisted of 12 items, 6 measuring a general privacy caution and 6 measuring technical protection [START_REF] Buchanan | Development of measures of online privacy concern and protection for use on the internet[END_REF]. To ensure consistency we modified the scale, and instead of Likert scale, we applied range scores. In a result, we collected continuous data with scores ranging from 0 to 100 (never/always).

Data collection

The online survey was distributed on two platforms, Microworkers and CallFor-Participants (CFP). Participation in the survey was voluntary. Microworkers' participants received financial compensation $1-$1.50 per response, while CFP respondents did not receive any compensation. The total number of participants reached 437 (375 from Microworkers, 62 from CallForParticipants), however, only 382 responses were valid. On Microworkers the response validity was checked automatically. Additionally, all responses were monitored manually, one by one. Furthermore, any surveys completed in less than five minutes or longer than four hours were removed. Participants had to respond to all questions and in a result, there was no missing data; the survey allowed respondents to backtrack and amend responses. Each respondent could participate in the survey only once. Furthermore, to decrease the possibility of statistical bias, the data set was scanned for outliers. As recommended in the literature, instead of using a standard method for detecting extreme cases, such as the mean plus/minus two or three standard deviations [START_REF] Leys | Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median[END_REF], we applied 3x Inter-quartile Range. All responses that contained outliers were removed from the analysis, which left the sample of 382 responses.

To assess the desired demographics, we used a geographic cluster sampling, with cluster sizes aiming to reach 100 respondents each. Choice of geographic areas was based on the results from the Data Protection Eurobarometer [START_REF]Special Eurobarometer 431 'Data Protection[END_REF]. We focused on four geographic areas: UK, USA, Italy and Nordic countries (Sweden, Norway, Finland, Denmark, and Germany). Among the respondents 57.9% (N = 221) were males and 42.1% (N = 161) females; the average age was 32 years (M in = 18; M ax = 70). The full demographics beak-down is presented in Table 2.

Dimensions of privacy concerns

To assess the answer to the RQ1 we commenced with investigating its subquestion:What are the dimensions of privacy concerns? (RQ1.1). We created the PHC and used the Exploratory Factor Analysis (EFA) to assess dimensions of privacy concerns.

The EFA was used because it allows to ascertain factors that may explain correlations between variables, but it does not require underlying theoretical structure [START_REF] Reio | Exploratory Factor Analysis: Implications for Theory, Research, and Practice[END_REF]. The Kaiser-Meyer-Olkin measure (.903) and Bartlett test for sphericity (significant at the level p < .001) confirmed EFA's suitability. We used orthogonal rotation, varimax presuming that the correlations between the variables are weak.

To extract factors, we used the principal axis factoring (PAF) allowing to measure the latent structure of variables and their relationships [START_REF] Reio | Exploratory Factor Analysis: Implications for Theory, Research, and Practice[END_REF]. From the original 48 items 30 items remained, after removing factors with communalities < .3, item loadings < .3 and factors consisting of less than three loaded items.

After applying the solution and scree plot analysis, we extracted seven factors, identifying people's perceptions of privacy concerns: unauthorized access, misuse of data, secondary use of data, insecurity, exposure, interrogation, distortion. When computing the internal consistency for the scale based on the factors, the Cronbach alpha scores for the identified factors were all above .7 (Table 3).

Additionally we computed the means for each dimension of the privacy concerns as demonstrated in Table 4. We used the means in further analysis, to assess the relationship with behavior and investigate demographics.

Information disclosure

To asses the differences in concern between respondents who disclose sensitive/nonsensitive items (RQ2.1 and RQ2.2) we performed the independent-sample t-Test.

We checked the outcomes of Levene's test that were significant at level > .05 , hence we report the results for equal variances not assumed. We found a significant difference among respondents that disclose (M=70.5, SD=20.6) and do not disclose (M=77.1, SD=17.5) sensitive information about the secondary use of data, t(380) = -2.9, p = .002; and interrogation (M=42.9, SD=21.4; M=53.3, SD=18.8 respectively), t(380) = -4.4, p < .001.

We identified the same type of concerns among participants disclosing nonsensitive information. The respondents who did not disclose information (M=77.6, SD=19) were significantly more concerned about the secondary use of data than those who disclose it (M=71.5, SD=20.1), t(380) = -2.1, p = .029; the same behavior was observed regarding interrogation (M=52.9, SD=21.9; M=44.6, SD=20.9 respectively), t(380) = -2.7, p = .010.

Protection behavior

To determine the relationship between privacy concerns and protection behaviors (RQ2.3 and RQ2.4) we performed Pearson Correlation tests, and examined scatter plots for the correlated variables. We identified significant correlations between general caution and technical protection behavior, and privacy concerns, ranging between r = .184 and r = .404 (Table 6). The results demonstrate positive correlations for general caution and concerns about unauthorized access, misuse of data, insecurity, exposure and distortions, and a negative correlation for interrogation. Similarly, positive correlations were found for technical protection behavior and unauthorized access, misuse of data, secondary use of data, insecurity, exposure and distortions. However, we did not identify a relationship between general caution and secondary use, as well as between technical protection and interrogation.

Demographics

We conducted One-Way Analysis of Variance (ANOVA), t-Tests and Chi-Square to analyze whether there are significant differences in privacy concerns among people from various demographics (RQ3).

First, we analyzed responses of participants from different geographic locations (Table 7). There were significant effects for secondary use of data (F (3, 381) = 5.010; p = .002), interrogation (F (3, 381) = 3.241; p = .022) and distortion (F (3, 381) = 2.885; p = .036). The post-hoc Tukey test results confirmed significant differences (p = .001) between Italy (M=77.2; SD=19.9) and the UK (M = 77.2; SD = 17.9) regarding the secondary use of data. Similarly, there was a significant difference (p = .038) between Italy (M =40. We performed One-way ANOVA and the post-hoc Tukey test to asses whether there are potential differences in privacy concerns, protection behavior and information disclosure among participants from different age groups. For this purpose we divided our sample to four age groups: 18 -24, 25 -34, 35 -44 and over 45 years old. We found a significant effect of age on concerns about the unauthorized access (F (3, 378) = 4.860, p = .002), misuse of data (F (3, 378) = 3.094, p = .027), secondary use of data (F (3, 378) = 3.162, p = .013), insecurity (F (3, 378) = 4.710, p = .003) and exposure (F (3, 378) = 3.759, p = .011). The participants belonging to 35 -44 and 18 -24 years old groups differed in perception about unauthorized access and misuse of data; over 45 and 18 -24 differed in perceptions of exposure; over 45 differed from 18 -24 and 25 -34 years old in perception of secondary use of data. Lastly, participants belonging to 35-44 and over 45 years old differed from the 18-24 years old in concerns about insecurity. We did not find any significant differences among participants from different age groups in relation to protection behavior and information disclosure.

Lastly, we used the independent t-Test to see whether there are significant gender differences about privacy concerns, but we did not find any p < .05. Similarly, we did not identify any gender dependencies in regards to both general caution and technical protection. Furthermore, we used Chi-Square test to determine whether the sensitive and non-sensitive information disclosure differed among males and females, however, once again the results were insignificant.

Discussion

To improve understanding of privacy perceptions we investigated privacy harms by creating the new scale measuring privacy concerns (RQ1). As we wanted to achieve a greater understanding of people's attitudes, we used the legal framework as a basis for the study design. The results demonstrated, that privacy perceptions vary from those identified by Solove. However, there are some resemblances. While Solove proposed to consider harms at the individual level, the results showed that people express privacy concerns differently. They tend to perceive concerns as comprehensive and simplified models. Possibly, such perception is related to the cognitive information processing, intending to decrease the cognitive effort and use affect heuristics.

We identified seven dimensions of privacy concerns: insecurity, exposure, unauthorized access, secondary use of data, misuse of data, distortion and interrogation. The analysis of the means suggests that people express high concerns about security. They want to be informed about data security breaches and in general, they expect that online services will guarantee safety. According to the findings, people worry about exposure, which may suggest that they care about online presence and information visibility. They want to be in control of personal information, ensuring that none of it is used without their knowledge or permission. The findings show general worries about the secondary use of data, such as selling or sharing data with external organizations, and about misuse of data, such as blackmail or malicious use of information by strangers to reach their own goals. Distortion seems to be less important, and interrogation is perceived as the least severe. Considering interrogation, paradoxically, respondents expressing concerns about secondary use or misuse of information did not find the information probing important. Overall, the new dimensions show similari-ties to Solove's findings. The results show that almost all of the harms defined by Solove are subject to concern, however, not at the individual level and not accordingly with the process of information flow. Additionally, it seems that invasions are the one group of harms which is perceived as less severe than others.

The identified dimensions of privacy concerns relate to findings from the past research. For instance an improper access and secondary use of data, the two of four dimensions defined by CFIP [START_REF] Smith | Information privacy: measuring individuals' concerns about organizational practices[END_REF]. Similarly, our findings relate to the factors identified by IUIPC: collection (interrogation and insecurity) and control (exposure, distortion) [START_REF] Malhotra | Internet users' information privacy concerns (iuipc): The construct, the scale, and a causal model[END_REF]. The seven dimensions of PHC add to the previous scales by identification of wider range of concerns. Our findings origin from participants with broad demographics, while CFIP was based on students and professionals from business environment, IUIPC was customer oriented. Furthermore, the PHC uncovers issues related to the self (me as a person and as a part of the society), such as distortion or exposure, showing that personal image, online reputation, fear of the damages, which could be caused by disclosed data are important factors causing privacy concerns.

Additionally, we investigated whether privacy perceptions differ among people who disclose sensitive and non-sensitive information (RQ2). The findings demonstrate that privacy concerns of participants who do not disclose both sensitive and non-sensitive information differ from those who disclose information. Respondents who do not disclose information expressed concerns about their data being sold to third parties and about providing feedback related their online activities. This result suggests that people concerned about their data ownership use preventive methods, such as non-disclosure, to ensure that none of their information, whether it has sensitive or non-sensitive nature, is provided to the online companies. Additionally, the results found that people's privacy concerns are the same among those who disclose/non-disclose sensitive and non-sensitive information. Presumably, if one worries about the privacy, he/she will behave in the same way regardless of information sensitivity.

Further, the study identified relationships between protection behaviors and privacy concerns (RQ2). Despite the low correlations between protection behaviors and privacy concerns, scatter-plots' analysis confirmed the relationships. Respondents with higher technical protection behavior seemed to have high concerns about the unauthorized access, misuse and secondary use of data, insecurity, exposure and distortions. The same applies to general caution, except there is no correlation with secondary use of data. Instead, the higher general caution, the higher interrogation concerns. Interestingly, our results did not find any correlation between technical protection and interrogation. This may suggest, that people using different technical protections may feel confident that data will not be sold or transferred to unknown organizations, because of users' preventive measures. On the other hand, it may be related to the fact, that people do not perceive interrogation as a very severe concern.

The demographic results indicate possible differences in privacy perceptions among respondents from different geographic locations, education and age groups (RQ3). We identified differences between respondents from different countries. This could imply the role of cultural diversity in shaping people's concerns. However, due to the small sample size, our findings are only an indication of possible cultural dependencies, which require further studies.

Considering other demographics, our results show that people from older generations express more concerns about privacy than the younger generations, confirming findings from the previous research [START_REF] Steijn | Why concern regarding privacy differs: The influence of age and (non-) participation on facebook[END_REF]. The age divide may be explained by the fact that older people have more experience, awareness, and knowledge related to privacy violations. Also, the younger population may use internet as a tool for communications, to develop social relationships or as a source of leisure activities, while older people may use it to cope with day-today activities, such as work, financial transactions, information source. For that reason, older generation may add more value to their online information, and in a result express stronger privacy concerns. On the other hand, as demonstrated in the past research, the younger generation may express fewer concerns due to their protection behaviors.

Limitations There is a number of limitations in this study. The method: selfreported survey, may decrease validity and reliability of the results. However, as the study was designed to reach international respondents within a short time, this method was the most effective. Similarly, the enlarged sample size could improve the results, especially the demographic assumptions. Furthermore, the research explored general privacy concerns and did not investigate whether they would change considering specific context, for instance, different technologies. The collected data did not allow to model causal relationships between concerns and behaviors. The investigation of causal relations could provide a better overview on the role of privacy concerns in the decision making.

Conclusion

This study contributes a new measurement instrument for privacy concerns. To differentiate it from the existing privacy scales, we aimed to shift the focus of privacy concerns to privacy harms, based on the framework developed by Solove. We demonstrated that identified privacy concerns vary among individuals, by analyzing self-reported behavior and demographics. The new instrument can be used in future studies assessing privacy attitudes.

Additionally, the results suggest that there are some general tendencies in privacy concerns. The findings show that people create simplified models of privacy harms, such as worries about security, unlawful use of data, disclosure or exposure. All of these concerns can be addressed by developers and designers to ensure privacy. Due to the similarities among people from different demographics, we can assume that there is a potential to build systems with 'privacy for all' or 'privacy with no borders'. Future work Our privacy scale requires further validation in qualitative and quantitative studies. For instance, to improve the scale it is recommended to implement it in experiments of the actual privacy behavior, using the PHC as pre-and/or post-questionnaire. Our results will be fundamental to develop models for instruments influencing peoples' behavior, nudging people's privacy choices and improving their privacy risk awareness. Similarly, further studies of PHC could result in the set of guidelines for developers and designers of privacy enhancing technologies (PET). Such guidelines could enable easier assessment of people's privacy needs, improving usability of PETs and in a result increasing users' satisfaction.

  3; SD = 20.5) and the Nordic Countries (M = 49.8; SD = 19.7), and Italy and the UK (M = 48.3; SD = 20.7), (p = .034) in concerns related to interrogation. Additionally, we found a significant difference (p = .017) between the USA (M = 68.4; SD = 20.1) and Nordic Countries (M = 60.6; SD = 19.7), and the USA and Italy (M = 60.4; SD = 23.0) about distortion (p = .010).

Table 1 .

 1 Typology of privacy harms according to the Solove's framework.

	Information Information Information	Invasions
	collection	processing dissemination
	Surveillance Aggregation Breach of confidentiality Intrusion
	Interrogation Identification Disclosure	Decisional interference
		Insecurity	Exposure
		Secondary Use Increased Accessibility
		Exclusion	Blackmail
			Appropriation
			Distortion
	2 Related work

2.1 Privacy attitudes: concerns and harms

Table 2 .

 2 Participants demographics

	Demographic N Percent
	Country
	Italy	91 23.8
	Nordic countries 76 19.9
	UK	113 29.6
	USA	102 26.7
	Gender
	Male	221 57.9
	Female	161 42.1
	Education
	High school	70 18.3
	Higher education 203 53.1
	Still studying	109 28.5
	Age
	18-24	98 25.7
	25-34	153 40.1
	35-44	76 19.9
	over 44	55 14.4
	Total	382

Table 3 .

 3 The results of Exploratory Factor Analysis; N = 382.

	Extracted factors	Cronbach alpha
	Factor 1: unauthorized access to data	.865
	Factor 2: misuse of data	.836
	Factor 3: secondary use of data	.811
	Factor 4: insecurity	.736
	Factor 5: exposure	.745
	Factor 6: interrogation	.721
	Factor 7: distortion	.735

Table 4 .

 4 Means of the privacy concerns dimensions, N =382.

	Dimension	M	SD
	Insecurity	90.02	10.4
	Exposure	77.82	17.7
	Unauthorized access	72.75	17.2
	Secondary use of data	72.42	20.0
	Misuse of data	71.23	16.1
	Distortion	63.75	21.5
	Interrogation	45.89	21.2

Table 5 .

 5 Correlations between privacy concerns and protection behaviors; N = 382.

		General caution Technical Protection
	Unauthorized access	.318**	.290**
	Misuse of data	.404**	.357**
	Secondary use	.024	.184**
	Insecurity	.201**	.346**
	Interrogation	-.243**	-.027
	Exposure	.215**	.233**
	Distortion	.358**	.246**

** Correlation is significant at the .001 level

(2-tailed)

Table 6 .

 6 Differences in privacy concerns among participants from different geographic areas; N = 382, p < .05 (One-Way ANOVA).

	Source	SS	df	MS	F	p
	Secondary use
	Between	5857.7	3	1952.5	5.0	.002
	Within	147327.7	378	389.7
	Total	153185.4	381
	Interrogation
	Between	4311.6	3	1437.2	3.2	.022
	Within	167642.2	378	443.4
	Total	171953.8	381
	Distortion
	Between	3974.4	3	1324.8	2.8	.036
	Within	173576.6	378	459.1
	Total	177551.0	381

Acknowledgment This work has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No 675730. To obtain more information about the study or to gain access to the original questionnaire, please contact the corresponding author.