How private is your period?: A systematic analysis of menstrual app ...
Volume: 2020 Issue: 4 Pages: 491–510 DOI: https://doi.org/10.2478/popets-2020-0083 Download PDF Abstract: Menstruapps are mobile applications that can track a user’s reproductive cycle, sex life and …
Proceedings on Privacy Enhancing Technologies ; 2020 (4):414–433 Payman Mohassel, Mike Rosulek, and Ni Trieu*
Practical Privacy-Preserving K-means Clustering
Volume: 2020 Issue: 4 Pages: 414–433 DOI: https://doi.org/10.2478/popets-2020-0080 Download PDF Abstract: Clustering is a common technique for data analysis, which aims to partition data into similar …
Pets without PETs: on pet owners’ under-estimation of privacy …
Volume: 2020 Issue: 1 Pages: 143–164 DOI: Download PDF Abstract: We report on a mixed-method, comparative study investigating whether there is a difference between privacy concerns expressed …
Proceedings on Privacy Enhancing Technologies ; 2020 (1):143–164 Dirk van der Linden*, Matthew Edwards, Irit Hadar, and Anna Zamansky
PoPETs Proceedings — FLASH: Fast and Robust Framework for Privacy ...
Volume: 2020 Issue: 2 Pages: 459–480 DOI: Download PDF Abstract: Privacy-preserving machine learning (PPML) via Secure Multi-party Computation (MPC) has gained momentum in the recent past.
Proceedings on Privacy Enhancing Technologies ; 2020 (4):131–152 Xihui Chen, Sjouke Mauw, and Yunior Ramírez-Cruz*
PoPETs Proceedings — The Best of Both Worlds: Mitigating Trade-offs ...
Volume: 2020 Issue: 1 Pages: 195–215 DOI: https://doi.org/10.2478/popets-2020-0011 Download PDF Abstract: In today’s data-centric economy, data flows are increasingly diverse and complex. This is …
Proceedings on Privacy Enhancing Technologies ; 2020 (2):175–208 Peeter Laud*, Alisa Pankova, and Martin Pettai
PoPETs Proceedings — SoK: Differential privacies
Volume: 2020 Issue: 2 Pages: 288–313 DOI: https://doi.org/10.2478/popets-2020-0028 Download PDF Abstract: Shortly after it was first introduced in 2006, differential privacy became the flagship data …