Paper 2023/073
FssNN: Communication-Efficient Secure Neural Network Training via Function Secret Sharing
Abstract
Privacy-preserving neural network based on secure multi-party computation (MPC) enables multiple parties to jointly train neural network models without revealing sensitive data. In privacy-preserving neural network, the high communication costs of securely computing non-linear functions is the primary performance bottleneck. For commonly used non-linear functions, such as ReLU, existing work adopts an offline-online computation paradigm and utilizes distributed comparison function (DCF) to reduce communication costs. Specifically, these works prepare DCF keys in the offline phase and perform secure ReLU using these DCF keys in the online phase. However, the practicality of existing work is significantly limited due to the substantial size of DCF keys and the heavy reliance on a trusted third party in the offline phase.
In this work, we introduce a communication-efficient secure two-party neural network framework called FssNN, which proposes a key-reduced DCF scheme without a trusted third party to enable practical secure training and inference. First, by analyzing the correlations between DCF keys to eliminate redundant parameters, we propose a key-reduced DCF scheme with a compact additive construction, which decreases the size of DCF keys by about
Note: This is an updated version, with revised authorship and acceptance information added.
Metadata
- Available format(s)
- Category
- Cryptographic protocols
- Publication info
- Published elsewhere. Minor revision. ProvSec 2024
- Keywords
- Privacy-preserving neural networkSecure multi-party computationAdditive secret sharingFunction secret sharing
- Contact author(s)
-
stuyangpeng @ stu hit edu cn
zoeljiang @ hit edu cn - History
- 2024-07-26: last of 5 revisions
- 2023-01-22: received
- See all versions
- Short URL
- https://ia.cr/2023/073
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2023/073, author = {Peng Yang and Zoe Lin Jiang and Shiqi Gao and Hongxiao Wang and Jun Zhou and Yangyiye Jin and Siu-Ming Yiu and Junbin Fang}, title = {{FssNN}: Communication-Efficient Secure Neural Network Training via Function Secret Sharing}, howpublished = {Cryptology {ePrint} Archive, Paper 2023/073}, year = {2023}, url = {https://eprint.iacr.org/2023/073} }