TY - GEN
T1 - Communication-Efficient Secure Neural Network via Key-Reduced Distributed Comparison Function
AU - Yang, Peng
AU - Jiang, Zoe Lin
AU - Gao, Shiqi
AU - Wang, Hongxiao
AU - Zhou, Jun
AU - Jin, Yangyiye
AU - Yiu, Siu Ming
AU - Fang, Junbin
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - 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 methods adopt an offline-online computation paradigm and utilizes distributed comparison function (DCF) to reduce communication costs. Specifically, these methods prepare DCF keys in the offline phase and perform secure non-linear function computation using these keys in the online phase. However, the practicality of these methods is limited due to the substantial size of DCF keys and the heavy reliance on a trusted third party during the offline phase. In this work, we introduce FssNN, a communication-efficient secure two-party neural network framework, which features a key-reduced DCF scheme without a trusted third party to enable practical secure training and inference. Firstly, by analyzing the correlations between DCF keys to eliminate redundant parameters, we propose a key-reduced DCF scheme with a compact additive construction, decreasing the size of DCF keys by about 17.9% and offline communication costs by approximately 28.0%. Secondly, leveraging an MPC-friendly pseudorandom number generator, we propose a secure two-party distributed key generation protocol for our key-reduced DCF, eliminating the need for a trusted third party. Finally, we utilize the key-reduced DCF and additive secret sharing to compute non-linear and linear functions, and design secure computation protocols with constant online communication rounds for neural network operations, reducing online communication costs by 28.9%–43.4%. We provide formal security proofs and evaluate the performance of FssNN on various models and datasets. Experimental results show that compared to the state-of-the-art framework AriaNN, our framework reduces the total communication costs of secure training and inference by approximately 25.4% and 26.4% respectively.
AB - 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 methods adopt an offline-online computation paradigm and utilizes distributed comparison function (DCF) to reduce communication costs. Specifically, these methods prepare DCF keys in the offline phase and perform secure non-linear function computation using these keys in the online phase. However, the practicality of these methods is limited due to the substantial size of DCF keys and the heavy reliance on a trusted third party during the offline phase. In this work, we introduce FssNN, a communication-efficient secure two-party neural network framework, which features a key-reduced DCF scheme without a trusted third party to enable practical secure training and inference. Firstly, by analyzing the correlations between DCF keys to eliminate redundant parameters, we propose a key-reduced DCF scheme with a compact additive construction, decreasing the size of DCF keys by about 17.9% and offline communication costs by approximately 28.0%. Secondly, leveraging an MPC-friendly pseudorandom number generator, we propose a secure two-party distributed key generation protocol for our key-reduced DCF, eliminating the need for a trusted third party. Finally, we utilize the key-reduced DCF and additive secret sharing to compute non-linear and linear functions, and design secure computation protocols with constant online communication rounds for neural network operations, reducing online communication costs by 28.9%–43.4%. We provide formal security proofs and evaluate the performance of FssNN on various models and datasets. Experimental results show that compared to the state-of-the-art framework AriaNN, our framework reduces the total communication costs of secure training and inference by approximately 25.4% and 26.4% respectively.
KW - Additive secret sharing
KW - Distributed comparison function
KW - Privacy-preserving neural network
KW - Secure multi-party computation
UR - https://www.scopus.com/pages/publications/85219172628
U2 - 10.1007/978-981-96-0957-4_8
DO - 10.1007/978-981-96-0957-4_8
M3 - 会议稿件
AN - SCOPUS:85219172628
SN - 9789819609567
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 143
EP - 163
BT - Provable and Practical Security - 18th International Conference, ProvSec 2024, Proceedings
A2 - Liu, Joseph K.
A2 - Chen, Liqun
A2 - Sun, Shi-Feng
A2 - Liu, Xiaoning
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Provable and Practical Security, ProvSec 2024
Y2 - 25 September 2024 through 27 September 2024
ER -