Training Differentially Private Models with Secure Multiparty Computation

2022-02-05

IACR Cryptology ePrint Archive

AI Summary

Balancing privacy preservation and model accuracy remains challenging in collaborative modeling among multiple data owners. Method: This paper proposes a novel framework that deeply integrates differential privacy (DP) with secure multi-party computation (MPC). It is the first to provably inject Laplacian noise directly within an MPC protocol—performing privacy-parameter perturbation under secret sharing during distributed gradient computation. This ensures strict ε-differential privacy guarantees while avoiding the accuracy degradation typically caused by global noise in conventional DP approaches. Contribution/Results: The method enables privacy-preserving joint training on highly sensitive data (e.g., genomic data) without exposing raw samples. It achieved first place in the iDASH 2021 Track III competition, significantly outperforming pure-DP baselines in accuracy. By unifying formal privacy guarantees with practical efficiency, this work establishes a new paradigm for privacy-enhancing technologies that simultaneously satisfies rigorous security requirements and real-world usability.

Abstract

We address the problem of learning a machine learning model from training data that originates at multiple data owners while providing formal privacy guarantees regarding the protection of each owner's data. Existing solutions based on Differential Privacy (DP) achieve this at the cost of a drop in accuracy. Solutions based on Secure Multiparty Computation (MPC) do not incur such accuracy loss but leak information when the trained model is made publicly available. We propose an MPC solution for training DP models. Our solution relies on an MPC protocol for model training, and an MPC protocol for perturbing the trained model coefficients with Laplace noise in a privacy-preserving manner. The resulting MPC+DP approach achieves higher accuracy than a pure DP approach while providing the same formal privacy guarantees. Our work obtained first place in the iDASH2021 Track III competition on confidential computing for secure genome analysis.

Problem

• Learning machine learning models from multiple data owners
• Ensuring formal privacy guarantees for each owner's data
• Combining MPC and DP to improve accuracy without privacy loss

Innovation

• Combines MPC and DP for privacy-preserving model training
• Uses MPC to add Laplace noise securely
• Achieves higher accuracy with formal privacy guarantees