Related papers: Efficient Private Inference Based on Helper-Assisted Malicious Security Dishonest Majority MPC

Efficient Private Inference Based on Helper-Assisted Malicious Security Dishonest Majority MPC

URL: http://arxiv.org/abs/2507.09607v3
Date: Mon, 04 Aug 2025 13:08:58 GMT
Title: Efficient Private Inference Based on Helper-Assisted Malicious Security Dishonest Majority MPC
Authors: Kaiwen Wang, Xiaolin Chang, Junchao Fan, Yuehan Dong,
Abstract summary: We propose a novel, three-layer private inference framework based on the Helper-Assisted MSDM model.<n>The framework achieves up to a 2.4-25.7x speedup in LAN and a 1.3-9.5x acceleration in WAN over the state-of-the-art MSDM frameworks.
Score: 5.797285315996385
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The existing MPC-based private inference frameworks either rely on impractical real-world assumptions, or adopt the strongest security model (Malicious Security Dishonest Majority, MSDM) and then suffer from severe efficiency limitations. To balance security and efficiency, we propose a novel, three-layer private inference framework based on the Helper-Assisted MSDM (HA-MSDM) model. The first is the primitive layer, where we extend computations from prime fields to rings for efficient fixed-point arithmetic and then better support inference operations. The second is the MPC layer, where we design six fixed-round MPC protocols to reduce latency for core operations like multiplication, polynomial evaluation, and batch check. The third is the inference layer, which can achieve efficient and high-accuracy CNN inference. The efficiency is achieved by applying our designed MPC protocols. The high-accuracy private inference in deep CNNs is achieved by designing a co-optimized strategy, which employs high-precision polynomial approximation for activation functions and uses parameter-adjusted Batch Normalization layers to constrain inputs. Benchmarks on LeNet and AlexNet show our framework achieves up to a 2.4-25.7x speedup in LAN and a 1.3-9.5x acceleration in WAN over the state-of-the-art MSDM frameworks with only 0.04-1.08% relative error.

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