Welcome to the resource topic for 2025/1315
Title:
CostSphere: A Cost Model-Driven Privacy-Preserving Machine Learning Framework with Network Context Adaptation
Authors: Yuntian Chen, Zhanyong Tang, Tianpei Lu, Bingsheng Zhang, Zhiying Shi, Zhiyuan Ning
Abstract:Privacy-preserving machine learning (PPML) is critical for protecting sensitive data in domains like healthcare, finance, and recommendation systems. Fully Homomorphic Encryption (FHE) and Secure Multi-Party Computation (MPC) are key enablers of secure computation, yet existing hybrid approaches often suffer from fixed protocol assignments, resulting in inefficiencies across diverse network environments, such as LANs and WANs. To address this, we introduce CostSphere, a cost-model-driven framework that dynamically assigns FHE and MPC protocols to optimize computational efficiency under varying network conditions. Utilizing a predictive cost model based on MLIR’s TOSA-level dialect and an ILP-based solver, CostSphere ensures robust performance for Transformer-based models. Experimental results demonstrate that CostSphere delivers 6.68\times to 12.92\times improvements in inference runtime compared to state-of-the-art solutions like BumbleBee (NDSS ’25), enabling scalable and network-agnostic PPML across diverse computational scenarios.
ePrint: https://eprint.iacr.org/2025/1315
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