Welcome to the resource topic for 2024/2021
Title:
PrivQuant: Communication-Efficient Private Inference with Quantized Network/Protocol Co-Optimization
Authors: Tianshi Xu, Shuzhang Zhong, Wenxuan Zeng, Runsheng Wang, Meng Li
Abstract:Private deep neural network (DNN) inference based on secure two-party computation (2PC) enables secure privacy protection for both the server and the client. However, existing secure 2PC frameworks suffer from a high inference latency due to enormous communication. As the communication of both linear and non-linear DNN layers reduces with the bit widths of weight and activation, in this paper, we propose PrivQuant, a framework that jointly optimizes the 2PC-based quantized inference protocols and the network quantization algorithm, enabling communication-efficient private inference. PrivQuant proposes DNN architecture-aware optimizations for the 2PC protocols for communication-intensive quantized operators and conducts graph-level operator fusion for communication reduction. Moreover, PrivQuant also develops a communication-aware mixed precision quantization algorithm to improve the inference efficiency while maintaining high accuracy. The network/protocol co-optimization enables PrivQuant to outperform prior-art 2PC frameworks. With extensive experiments, we demonstrate PrivQuant reduces communication by 11\times, 2.5\times \mathrm{and}~ 2.8\times, which results in 8.7\times, 1.8\times ~ \mathrm{and}~ 2.4\times latency reduction compared with SiRNN, COINN, and CoPriv, respectively.
ePrint: https://eprint.iacr.org/2024/2021
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