Privacy-Preserving Diffusion Adaptive Learning With Nonzero-Mean Protection Noise
针对自适应网络分布式学习中的数据隐私问题，提出一种使用非零均值保护噪声的新型隐私保护扩散自适应最小均方算法，通过均方分析评估其稳定性与稳态误差，并通过仿真验证有效性。
In this article, we consider the data privacy issue of distributed learning over adaptive networks under zero-mean protection noise. First, using a nonzero-mean protection noise, a new privacy-preserving diffusion adaptive least-mean-squares algorithm is devised, named NZPD-LMS. Different from the existing differential privacy noise, the nonzero-mean protection noise is designed with two noises with zero-mean and nonzero-mean, allowing the zero-mean noise to retain differential privacy properties, and the nonzero-mean noise to prevent the use of a sliding average over time to obtain transmission values. Then, based on mean-square analysis, we evaluate stability conditions and steady-state error bounds for the NZPD-LMS algorithm, as well as how each algorithmic parameter affects steady-state error. Finally, several simulations are conducted to illustrate the theoretical findings and effectiveness of the proposed approach.