1st PEN-NL workshop
The first workshop of Privacy Engineering Network of the Netherlands (PEN-NL) was held at Radboud University, Nijmegen on July 14, 2022.
- 14:00 Welcome
- 14:05 Hugo Jonker, OU: “How to use twitter whilst ensuring privacy”
- 14:35 Bart Voorn, STЯM Privacy: “Enforce privacy compliance at scale: highlighting a privacy by design architecture for data management systems”
- 15:05 Break
- 15:20 Thijs Veugen, TNO/CWI/UT: “Privacy-preserving Survival Analysis”
- 15:50 Organisational matters, future plans, and AOB.
14:05 Hugo Jonker, OU: “How to use twitter whilst ensuring privacy”
Social networks allow users to interact with each other in a simple way. However, keeping your updates private is not always as straightforward. A significant challenge in this respect is that social networks track how users interact with other users’ content. So even if you manage to limit who can see your updates, that group necessarily includes the social network itself. This can even lead to a chilling effect, where users who wish to visit a repressive country fear following a prominent dissident (e.g., visitors to the USA and Edward Snowden or Julian Assange). In this talk, we explore a way to add a layer on top of a social network to ensure that the metadata of user interaction is obscured from the network operator.
14:35 Bart Voorn, STЯM Privacy: “Enforce privacy compliance at scale: highlighting a privacy by design architecture for data management systems”
In this short talk, I will first briefly explain the two main assumptions around privacy that we build upon when designing a data management system. Secondly, it is hard to balance access to data, velocity and innovative product building with data privacy. We will be exploring if there are ways to balance the two. Third, we will provide an example of the possibility to enforce compliance beyond a paper reality.
15:15 Thijs Veugen, TNO/CWI/UT: “Privacy-preserving Survival Analysis”
The Netherlands Comprehensive Cancer Organisation performs survival analysis on distributed data. Doing this in a privacy-preserving way is especially challenging for vertically-partitioned data, where separate organisations have different information on the same people. We present a solution to compute a so-called Cox proportional hazards model in that setting using secure multi-party computation.
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