PoPETs Proceedings — Falcon: Honest-Majority Maliciously Secure ...
Volume: 2021 Issue: 1 Pages: 188–208 DOI: Download PDF Abstract: We propose Falcon, an end-to-end 3-party protocol for efficient private training and inference of large machine …
PoPETs Proceedings
Volume 2025 Volume 2024 Volume 2023 Volume 2022 Volume 2021 Volume 2020 Volume 2019 Volume 2018 Volume 2017 Volume 2016 Volume 2015 Privacy Enhancing Technologies …
Growing synthetic data through differentially-private vine copulas
Volume: 2021 Issue: 3 Pages: 122–141 DOI: https://doi.org/10.2478/popets-2021-0040 Download PDF Abstract: In this work, we propose a novel approach for the synthetization of data based …
Proceedings on Privacy Enhancing Technologies ; 2021 (3):122–141 Sébastien Gambs, Frédéric Ladouceur, Antoine Laurent, and Alexandre Roy-Gaumond*
Efficient homomorphic evaluation of k-NN classifiers
Authors: Martin Zuber (CEA, LIST), Renaud Sirdey (CEA, LIST) Volume: 2021 Issue: 2 Pages: 111–129 DOI: https://doi.org/10.2478/popets-2021-0020 Download PDF Abstract: We design …
Controlled Functional Encryption Revisited: Multi-Authority …
Volume: 2021 Issue: 1 Pages: 21–42 DOI: https://doi.org/10.2478/popets-2021-0003 Download PDF Abstract: In a Functional Encryption scheme (FE), a trusted authority enables designated …
Multiparty Homomorphic Encryption from Ring-Learning-with-Errors
Volume: 2021 Issue: 4 Pages: 291–311 DOI: Download PDF Abstract: We propose and evaluate a securemultiparty-computation (MPC) solution in the semihonest model with dishonest …
Keywords: decision-tree induction, collaborative learn-ing, privacy-preserving protocols, leakage analysis DOI 10.2478/popets-2021-0043 Received 2020-11-30; revised 2021-03-15; accepted …
PoPETs Proceedings — Automated Extraction and Presentation of …
Volume: 2021 Issue: 2 Pages: 88–110 DOI: Download PDF Abstract: Privacy policies are documents required by law and regulations that notify users of the collection, use, and sharing …
PoPETs Proceedings — privGAN: Protecting GANs from …
Volume: 2021 Issue: 3 Pages: 142–163 DOI: Download PDF Abstract: Generative Adversarial Networks (GANs) have made releasing of synthetic images a viable approach to share data …