HELiKs: HE Linear Algebra Kernels for Secure Inference
Author:
Affiliation:
1. University of California San Diego, San Diego, CA, USA
Funder
Intel Corporation
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3576915.3623136
Reference43 articles.
1. Ahmad Al Badawi Jack Bates Flavio Bergamaschi David Bruce Cousins Saroja Erabelli Nicholas Genise Shai Halevi Hamish Hunt Andrey Kim Yongwoo Lee Zeyu Liu Daniele Micciancio Ian Quah Yuriy Polyakov Saraswathy R.V. Kurt Rohloff Jonathan Saylor Dmitriy Suponitsky Matthew Triplett Vinod Vaikuntanathan and Vincent Zucca. 2022. OpenFHE: Open-Source Fully Homomorphic Encryption Library. Cryptology ePrint Archive Paper 2022/915. https://eprint.iacr.org/2022/915 https://eprint.iacr.org/2022/915.
2. Marcelo Blatt Alexander Gusev Yuriy Polyakov and Shafi Goldwasser. 2020. Secure large-scale genome-wide association studies using homomorphic encryption. Cryptology ePrint Archive Paper 2020/563. https://doi.org/10.1073/pnas.1918257117 https://eprint.iacr.org/2020/563.
3. Fabian Boemer Rosario Cammarota Daniel Demmler Thomas Schneider and Hossein Yalame. 2020. MP2ML: A Mixed-Protocol Machine Learning Framework for Private Inference. Cryptology ePrint Archive Paper 2020/721. https://eprint.iacr.org/2020/721 https://eprint.iacr.org/2020/721.
4. Fabian Boemer Anamaria Costache Rosario Cammarota and Casimir Wierzynski. 2019. nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data. Cryptology ePrint Archive Paper 2019/947. https://eprint.iacr.org/2019/947 https://eprint.iacr.org/2019/947.
5. Efficient Pseudorandom Correlation Generators: Silent OT Extension and More
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