Platform Privacy: The Missing Piece of Data Protection Legislation

After years of deliberation, the EU commission sped up the reform process of a common EU digital policy considerably in 2015 by launching the EU digital single market strategy. In particular, two core initiatives of the strategy were agreed upon: General Data Protection Regulation and the Network and Information Security (NIS) Directive law texts. A new initiative was additionally launched addressing the role of online platforms. This paper focuses on the platform privacy rationale behind the data protection legislation, primarily based on the proposal for a new EU wide General Data Protection Regulation. We analyse the legislation rationale from an Information System perspective to understand the role user data plays in creating platforms that we identify as “processing silos”. Generative digital infrastructure theories are used to explain the innovative mechanisms that are thought to govern the notion of digitalization and successful business models that are affected by digitalization. We foresee continued judicial data protection challenges with the now proposed Regulation as the adoption of the “Internet of Things” continues. The findings of this paper illustrate that many of the existing issues can be addressed through legislation from a platform perspective. We conclude by proposing three modifications to the governing rationale, which would not only improve platform privacy for the data subject, but also entrepreneurial efforts in developing intelligent service platforms. The first modification is aimed at improving service differentiation on platforms by lessening the ability of incumbent global actors to lock-in the user base to their service/platform. The second modification posits limiting the current unwanted tracking ability of syndicates, by separation of authentication and data store services from any processing entity. Thirdly, we propose a change in terms of how security and data protection policies are reviewed, suggesting a third party auditing procedure.

Data sensitivity detection in chat interactions for privacy protection

In recent years, there has been exponential growth in using virtual spaces, including dialogue systems, that handle personal information. The concept of personal privacy in the literature is discussed and controversial, whereas, in the technological field, it directly influences the degree of reliability perceived in the information system (privacy ‘as trust’). This work aims to protect the right to privacy on personal data (GDPR, 2018) and avoid the loss of sensitive content by exploring sensitive information detection (SID) task. It is grounded on the following research questions: (RQ1) What does sensitive data mean? How to define a personal sensitive information domain? (RQ2) How to create a state-of-the-art model for SID?(RQ3) How to evaluate the model? RQ1 theoretically investigates the concepts of privacy and the ontological state-of-the-art representation of personal information. The Data Privacy Vocabulary (DPV) is the taxonomic resource taken as an authoritative reference for the definition of the knowledge domain. Concerning RQ2, we investigate two approaches to classify sensitive data: the first - bottom-up - explores automatic learning methods based on transformer networks, the second - top-down - proposes logical-symbolic methods with the construction of privaframe, a knowledge graph of compositional frames representing personal data categories. Both approaches are tested. For the evaluation - RQ3 – we create SPeDaC, a sentence-level labeled resource. This can be used as a benchmark or training in the SID task, filling the gap of a shared resource in this field. If the approach based on artificial neural networks confirms the validity of the direction adopted in the most recent studies on SID, the logical-symbolic approach emerges as the preferred way for the classification of fine-grained personal data categories, thanks to the semantic-grounded tailor modeling it allows. At the same time, the results highlight the strong potential of hybrid architectures in solving automatic tasks.

Security and privacy of resource constrained devices

The thesis aims to present a comprehensive and holistic overview on cybersecurity and privacy & data protection aspects related to IoT resource-constrained devices. Chapter 1 introduces the current technical landscape by providing a working definition and architecture taxonomy of ‘Internet of Things’ and ‘resource-constrained devices’, coupled with a threat landscape where each specific attack is linked to a layer of the taxonomy. Chapter 2 lays down the theoretical foundations for an interdisciplinary approach and a unified, holistic vision of cybersecurity, safety and privacy justified by the ‘IoT revolution’ through the so-called infraethical perspective. Chapter 3 investigates whether and to what extent the fast-evolving European cybersecurity regulatory framework addresses the security challenges brought about by the IoT by allocating legal responsibilities to the right parties. Chapters 4 and 5 focus, on the other hand, on ‘privacy’ understood by proxy as to include EU data protection. In particular, Chapter 4 addresses three legal challenges brought about by the ubiquitous IoT data and metadata processing to EU privacy and data protection legal frameworks i.e., the ePrivacy Directive and the GDPR. Chapter 5 casts light on the risk management tool enshrined in EU data protection law, that is, Data Protection Impact Assessment (DPIA) and proposes an original DPIA methodology for connected devices, building on the CNIL (French data protection authority) model.

Privacy and security enhanced offline oblivious transfer for massive data distribution

Unauthorized accesses to digital contents are serious threats to international security and informatics. We propose an offline oblivious data distribution framework that preserves the sender's security and the receiver's privacy using tamper-proof smart cards. This framework provides persistent content protections from digital piracy and promises private content consumption.

Privacy and Data Protection in Elderly Healthcare: Threats and Legal Warranties

The progressive aging of the population requires new kinds of social and medical intervention and the availability of different services provided to the elder population. New applications have been developed and some services are now provided at home, allowing the older people to stay home instead of having to stay in hospitals. But an adequate response to the needs of the users will imply a high percentage of use of personal data and information, including the building up and maintenance of user profiles, feeding the systems with the data and information needed for a proactive intervention in scheduling of events in which the user may be involved. Fundamental Rights may be at stake, so a legal analysis must also be considered.

TSKY: a dependable middleware solution for data privacy using public storage clouds

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

ADSNARK: Nearly practical and privacy-preserving proofs on authenticated data

We study the problem of privacy-preserving proofs on authenticated data, where a party receives data from a trusted source and is requested to prove computations over the data to third parties in a correct and private way, i.e., the third party learns no information on the data but is still assured that the claimed proof is valid. Our work particularly focuses on the challenging requirement that the third party should be able to verify the validity with respect to the specific data authenticated by the source — even without having access to that source. This problem is motivated by various scenarios emerging from several application areas such as wearable computing, smart metering, or general business-to-business interactions. Furthermore, these applications also demand any meaningful solution to satisfy additional properties related to usability and scalability. In this paper, we formalize the above three-party model, discuss concrete application scenarios, and then we design, build, and evaluate ADSNARK, a nearly practical system for proving arbitrary computations over authenticated data in a privacy-preserving manner. ADSNARK improves significantly over state-of-the-art solutions for this model. For instance, compared to corresponding solutions based on Pinocchio (Oakland’13), ADSNARK achieves up to 25× improvement in proof-computation time and a 20× reduction in prover storage space.

On privacy-awareness in social networking services

Magdeburg, Univ., Fak. für Informatik, Diss., 2011

Adaptive Privacy Management System Design For Context-Aware Mobile Devices

While mobile technologies can provide great personalized services for mobile users, they also threaten their privacy. Such personalization-privacy paradox are particularly salient for context aware technology based mobile applications where user's behaviors, movement and habits can be associated with a consumer's personal identity. In this thesis, I studied the privacy issues in the mobile context, particularly focus on an adaptive privacy management system design for context-aware mobile devices, and explore the role of personalization and control over user's personal data. This allowed me to make multiple contributions, both theoretical and practical. In the theoretical world, I propose and prototype an adaptive Single-Sign On solution that use user's context information to protect user's private information for smartphone. To validate this solution, I first proved that user's context is a unique user identifier and context awareness technology can increase user's perceived ease of use of the system and service provider's authentication security. I then followed a design science research paradigm and implemented this solution into a mobile application called "Privacy Manager". I evaluated the utility by several focus group interviews, and overall the proposed solution fulfilled the expected function and users expressed their intentions to use this application. To better understand the personalization-privacy paradox, I built on the theoretical foundations of privacy calculus and technology acceptance model to conceptualize the theory of users' mobile privacy management. I also examined the role of personalization and control ability on my model and how these two elements interact with privacy calculus and mobile technology model. In the practical realm, this thesis contributes to the understanding of the tradeoff between the benefit of personalized services and user's privacy concerns it may cause. By pointing out new opportunities to rethink how user's context information can protect private data, it also suggests new elements for privacy related business models.

ARCHITECTING USER-CENTRIC PRIVACY-AS-A-SET-OF-SERVICES: DIGITAL IDENTITY RELATED PRIVACY FRAMEWORK

AbstractDigitalization gives to the Internet the power by allowing several virtual representations of reality, including that of identity. We leave an increasingly digital footprint in cyberspace and this situation puts our identity at high risks. Privacy is a right and fundamental social value that could play a key role as a medium to secure digital identities. Identity functionality is increasingly delivered as sets of services, rather than monolithic applications. So, an identity layer in which identity and privacy management services are loosely coupled, publicly hosted and available to on-demand calls could be more realistic and an acceptable situation. Identity and privacy should be interoperable and distributed through the adoption of service-orientation and implementation based on open standards (technical interoperability). Ihe objective of this project is to provide a way to implement interoperable user-centric digital identity-related privacy to respond to the need of distributed nature of federated identity systems. It is recognized that technical initiatives, emerging standards and protocols are not enough to guarantee resolution for the concerns surrounding a multi-facets and complex issue of identity and privacy. For this reason they should be apprehended within a global perspective through an integrated and a multidisciplinary approach. The approach dictates that privacy law, policies, regulations and technologies are to be crafted together from the start, rather than attaching it to digital identity after the fact. Thus, we draw Digital Identity-Related Privacy (DigldeRP) requirements from global, domestic and business-specific privacy policies. The requirements take shape of business interoperability. We suggest a layered implementation framework (DigldeRP framework) in accordance to model-driven architecture (MDA) approach that would help organizations' security team to turn business interoperability into technical interoperability in the form of a set of services that could accommodate Service-Oriented Architecture (SOA): Privacy-as-a-set-of- services (PaaSS) system. DigldeRP Framework will serve as a basis for vital understanding between business management and technical managers on digital identity related privacy initiatives. The layered DigldeRP framework presents five practical layers as an ordered sequence as a basis of DigldeRP project roadmap, however, in practice, there is an iterative process to assure that each layer supports effectively and enforces requirements of the adjacent ones. Each layer is composed by a set of blocks, which determine a roadmap that security team could follow to successfully implement PaaSS. Several blocks' descriptions are based on OMG SoaML modeling language and BPMN processes description. We identified, designed and implemented seven services that form PaaSS and described their consumption. PaaSS Java QEE project), WSDL, and XSD codes are given and explained.

Categorization of bibsonomy tags to apply privacy-preserving mechanisms.

In this technical report, we approach one of the practical aspects when it comes to represent users' interests from their tagging activity, namely the categorization of tags into high-level categories of interest. The reason is that the representation of user profiles on the basis of the myriad of tags available on the Web is certainly unfeasible from various practical perspectives; mainly concerningthe unavailability of data to reliably, accurately measure interests across such fine-grained categorization, and, should the data be available, its overwhelming computational intractability. Motivated by this, our study presents the results of a categorization process whereby a collection of tags posted at BibSonomy #http://www.bibsonomy.org# are classified into 5 categories of interest. The methodology used to conduct such categorization is in line with other works in the field.

Nurse educators’ perceptions of the way nursing students protect patient privacy

The aim of this study was to assess the teaching-learning process related to patient privacy during the care process and the way nursing students’ protect patient privacy. Descriptive/correlational study using a qualitative approach and nonprobability sampling of 19 nurse educators from two schools of nursing. Data was collected using semi-structured interviews. Data analysis was undertaken using the SPSS version 20 and Alceste 2010 programs. The study complied with ethical standards. Two classes were assigned (protection of patient privacy and care process) with four subcategories (protection, empathy, relational competencies and technoscientific competencies).The findings show the need to adopt a reflective approach to the teaching-learning process by using experiential learning activities and real-life activities. We believe that intimacy and the protection of privacy should be core themes of nurse education and training.