AQ2PNN: Enabling Two-party Privacy-Preserving Deep Neural Network Inference with Adaptive Quantization-Reference-Cited by-同舟云学术

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AQ2PNN: Enabling Two-party Privacy-Preserving Deep Neural Network Inference with Adaptive Quantization

Published:2023-10-28 Issue: Volume: Page:
ISSN:
Container-title:56th Annual IEEE/ACM International Symposium on Microarchitecture
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Author:

Luo Yukui¹^ORCID,Xu Nuo²^ORCID,Peng Hongwu³^ORCID,Wang Chenghong⁴^ORCID,Duan Shijin⁵^ORCID,Mahmood Kaleel³^ORCID,Wen Wujie⁶^ORCID,Ding Caiwen³^ORCID,Xu Xiaolin⁵^ORCID

Affiliation:

1. Northeastern University, USA and University of Massachusetts Dartmouth, USA

2. Lehigh University, USA

3. University of Connecticut, USA

4. Duke University, United States of America and Indiana University, USA

5. Northeastern University, USA

6. North Carolina State University, USA

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3613424.3614297

Reference60 articles.

1. PUMA

2. High-Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority

3. Jeff Barnes . 2015. Azure machine learning. Microsoft Azure Essentials . 1 st ed, Microsoft ( 2015 ). Jeff Barnes. 2015. Azure machine learning. Microsoft Azure Essentials. 1st ed, Microsoft (2015).

4. Donald Beaver . 1992 . Efficient multiparty protocols using circuit randomization. In Advances in Cryptology—CRYPTO’91 : Proceedings 11 . Springer, 420–432. Donald Beaver. 1992. Efficient multiparty protocols using circuit randomization. In Advances in Cryptology—CRYPTO’91: Proceedings 11. Springer, 420–432.

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