Proceedings on Privacy Enhancing Technologies ; 2022 (1):353–372 Nishanth Chandran*, Divya Gupta, and Akash Shah

Volume: 2022 Issue: 1 Pages: 339–352 DOI: https://doi.org/10.2478/popets-2022-0017 Download PDF Abstract: Many data analysis operations can be expressed as a GROUP BY query on an unbounded …

Circuit-PSI With Linear Complexity via Relaxed Batch OPPRF Authors: Nishanth Chandran (Microsoft Research.), Divya Gupta (Microsoft Research.), Akash Shah (UCLA. Work done at Microsoft …

Volume: 2022 Issue: 1 Pages: 481–500 DOI: Download PDF Abstract: Business intelligence and AI services often involve the collection of copious amounts of multidimensional personal data. Since …

Proceedings on Privacy Enhancing Technologies ; 2022 (4):486–506 Simon Koch*, Malte Wessels, Benjamin Altpeter, Madita Olvermann, and Martin Johns

Proceedings on Privacy Enhancing Technologies ; 2022 (2):378–406 Saikrishna Badrinarayanan*, Peihan Miao, and Tiancheng Xie

Volume: 2022 Issue: 4 Pages: 486–506 DOI: Download PDF Abstract: At the end of 2020, Apple introduced privacy nutritional labels, requiring app developers to state what data is collected by their …

Volume: 2022 Issue: 4 Pages: 684–704 DOI: https://doi.org/10.56553/popets-2022-0128 Download PDF Abstract: We present ARQ, a systematic framework for creating cryptographic schemes that handle …

Pika: Secure Computation using Function Secret Sharing over Rings Authors: Sameer Wagh (Devron Corporation and UC Berkeley) Volume: 2022 Issue: 4 Pages: 351–377 DOI: Download PDF

Volume: 2022 Issue: 1 Pages: 291–316 DOI: Download PDF Abstract: We propose AriaNN, a low-interaction privacy-preserving framework for private neural network training and inference on …