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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). PETs attempt to protect the privacy of individuals whilst preserving the utility of data. One of the most popular technologies recently is 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.

On the 25th of May 2018 the EU will start to enforce the General Data Protection Regulation (EU-GDPR) [3]. This new regulation will replace the old Data Protection Act from 1998 and will disrupt common data processing practices. While the new regulation will make it easier to develop systems that comply with data protection laws all over Europe, it will change the way we design technology. With data protection a much more important factor and huge fines for data protection violations, technology vendors will demand systems where data protection was already considered during development. This will force the research community to broaden their perspective and consider how to develop and design systems in a way, that complies with data protection. This paper focuses on some of the more important parts of the GDPR for Assistive Environments. Reading the paper will not solve all your privacy related challenges but will help you to know which questions to ask. CCS CONCEPTS• Security and privacy → Privacy protections; Social aspects of security and privacy; Usability in security and privacy;