Keywords: Secure Multi-Party Computation, Privacy-preserving deep learning DOI 10.2478/popets-2019-0035 Received 2018-11-30; revised 2019-03-15; accepted 2019-03-16.

Volume: 2019 Issue: 4 Pages: 292–310 DOI: https://doi.org/10.2478/popets-2019-0070 Download PDF Abstract: In recent years, there have been several works that use website fingerprinting techniques …

Volume: 2019 Issue: 3 Pages: 26–49 DOI: Download PDF Abstract: Neural Networks (NN) provide a powerful method for machine learning training and inference. To effectively train, it is desirable for …

Proceedings on Privacy Enhancing Technologies ; 2019 (3):66–86 an Zimmeck*, Peter Story*, Daniel Smullen, Abhilasha Ravichander, Ziqi Wang, Joel Reidenberg,

p1-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning Authors: Se Eun Oh (University of Minnesota), Saikrishna Sunkam (University of Minnesota), Nicholas Hopper …

Authors: Johannes K Becker (Boston University), David Li (Boston University), David Starobinski (Boston University) Volume: 2019 Issue: 3 Pages: 50–65 DOI: https://doi.org/10.2478/popets-2019 …

Keywords: machine learning; privacy; inference attacks DOI 10.2478/popets-2019-0008 Received 2018-05-31; revised 2018-09-15; accepted 2018-09-16.

Proceedings on Privacy Enhancing Technologies ; 2019 (2):187–208 Ágnes Kiss*, Masoud Naderpour, Jian Liu, N. Asokan, and Thomas Schneider

Volume: 2019 Issue: 3 Pages: 66–86 DOI: Download PDF Abstract: The app economy is largely reliant on data collection as its primary revenue model. To comply with legal requirements, app developers …

Volume: 2019 Issue: 4 Pages: 6–33 DOI: Download PDF Abstract: Most encrypted data formats leak metadata via their plaintext headers, such as format version, encryption schemes used, number of …