Erik Krempel

The increasing availability and use of sensitive personal data raises a set of issues regarding the privacy of the individuals behind the data. These concerns become even more important when health data are processed, as are considered sensitive (according to most global regulations). Privacy Enhancing Technologies (PETs) attempt to protect the privacy of individuals whilst preserving the utility of data. One of the most popular technologies recently is Differential Privacy (DP), which was used for the 2020 U.S. Census. Another trend is to combine synthetic data generators with DP to create so-called private synthetic data generators. The objective is to preserve statistical properties as accurately as possible, while the generated data should be as different as possible compared to the original data regarding private features. While these technologies seem promising, there is a gap between academic research on DP and synthetic data and the practical application and evaluation of these techniques for real-world use cases. In this paper, we evaluate three different private synthetic data generators (MWEM, DP-CTGAN, and PATE-CTGAN) on their use-case-specific privacy and utility. For the use case, continuous heart rate measurements from different individuals are analyzed. This work shows that private synthetic data generators have tremendous advantages over traditional techniques, but also require in-depth analysis depending on the use case. Furthermore, it can be seen that each technology has different strengths, so there is no clear winner. However, DP-CTGAN often performs slightly better than the other technologies, so it can be recommended for a continuous medical data use case.

Digitization is becoming more and more important in the medical sector. Through electronic health records and the growing amount of digital data of patients available, big data research finds an increasing amount of use cases. The rising amount of data and the imposing privacy risks can be overwhelming for patients, so they can have the feeling of being out of control of their data. Several previous studies on digital consent have tried to solve this problem and empower the patient. However, there are no complete solution for the arising questions yet. This paper presents the concept of Sovereign Digital Consent by the combination of a consent privacy impact quantification and a technology for proactive sovereign consent. The privacy impact quantification supports the patient to comprehend the potential risk when sharing the data and considers the personal preferences regarding acceptance for a research project. The proactive dynamic consent implementation provides an implementation for fine granular digital consent, using medical data categorization terminology. This gives patients the ability to control their consent decisions dynamically and is research friendly through the automatic enforcement of the patients’ consent decision. Both technologies are evaluated and implemented in a prototypical application. With the combination of those technologies, a promising step towards patient empowerment through Sovereign Digital Consent can be made.

In times of strongly (personal) data-driven economy, the inception of the European General Data Protection Regulation (GDPR) recently reinforced the call for transparency and informational self-determination—not only due to the penalties for data protection violations becoming significantly more severe. This paper recaps the GDPR articles that should be noticed by software designers and developers and explains how, from the perspective of computer scientists, the summarized requirements can be implemented based on state-of-the-art technologies, such as data provenance tracking, distributed usage control, and remote attestation protocols. For this, the challenges for data controllers, i.e., the service providers, as well as for the data subjects, i.e., the users whose personal data are being processed by the services, are worked out. As a result, this paper proposes the ideal functionality of a next-generation privacy dashboard interacting with data provenance and usage control infrastructure implemented at the service providers to operationalize the legal rights of the data subject granted by the GDPR. Finally, it briefly outlines the options for establishing trust in data provenance tracking and usage control infrastructures operated by the service providers themselves.

Pervasive Environments (PE) collect and process a massive amount of person-related and sensitive information. Data collected by a single sensor is in most cases not adequate to provide premium services. Information gathered must rather be combined to offer real benefits. The fused data must be secured by access controls to ensure privacy of the users and their trust in PE with it. This work proposes an Object-oriented World Model (OOWM) as a central information source that is filled with information collected from intelligent sensors and can be accessed and manipulated by smart application devices. It is shown how privacy can be enforced in such a centralized component. Privacy requirements must be specified and enforced. Especially conflicts in different requirements, e. g., user- and operator-specific polices, is an open issue. Existing approaches for specification and enforcement of access controls are discussed. An XACML-based approach for privacy in PE is shown and an algorithm for combining privacy policies is presented.