Game-theoretical analysis of strategic information transmission for gradient privacy protection
Hanyuan Jiang, Song Wanjun, Long Bai, Yuzhe Li, Tianyou Chai · Journal of Control and Decision · 2025
Privacy protection of shared gradients in machine learning model training is crucial, yet the coexistence of honest-curious learners and malicious eavesdroppers in the real world is overlooked in research. A linear strategy-based privacy protection method for three-party games with eavesdroppers is proposed to address privacy threats in machine learning model training. Firstly, a three-party game framework is designed based on machine learning model training, with the data owner as the leader and the learner and eavesdropper as followers, leading to a privacy protection optimisation problem. Secondly, two eavesdropping scenarios are considered: limited and complete encoded information. By solving the equilibrium solution of the optimisation problem, the optimal strategy pair is obtained, and a strategic privacy protection information transmission mechanism is proposed. Finally, simulation experiments illustrate the theoretical results, validating the effectiveness of the algorithms proposed in this paper.