Towards Fast and Scalable Private Inference-Reference-Cited by-同舟云学术

Towards Fast and Scalable Private Inference

Published:2023-05-09 Issue: Volume: Page:
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Container-title:Proceedings of the 20th ACM International Conference on Computing Frontiers
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Author:

Mo Jianqiao¹^ORCID,Garimella Karthik¹^ORCID,Neda Negar¹^ORCID,Ebel Austin¹^ORCID,Reagen Brandon¹^ORCID

Affiliation:

1. New York University, New York, New York, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3587135.3592169

Reference45 articles.

1. Donald Beaver . 1995. Precomputing oblivious transfer . In Advances in Cryptology --- CRYPTO' 95 . Don Coppersmith, (Ed.) Springer Berlin Heidelberg , Berlin, Heidelberg , 97--109. isbn: 978-3-540-44750-4. Donald Beaver. 1995. Precomputing oblivious transfer. In Advances in Cryptology --- CRYPTO' 95. Don Coppersmith, (Ed.) Springer Berlin Heidelberg, Berlin, Heidelberg, 97--109. isbn: 978-3-540-44750-4.

2. VIP-Bench: A Benchmark Suite for Evaluating Privacy-Enhanced Computation Frameworks

3. Brent Carmer Alex J Malozemoff and Marc Rosen. 2019. Swanky: a suite of rust libraries for secure multi-party computation. (2019). Brent Carmer Alex J Malozemoff and Marc Rosen. 2019. Swanky: a suite of rust libraries for secure multi-party computation. (2019).

4. TFHE: Fast Fully Homomorphic Encryption Over the Torus

5. Sphynx: A Deep Neural Network Design for Private Inference

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