Time-Selective Privacy Protection for Network Communication and Its Application to Adaptive Prescribed-Performance Consensus Tracking

This paper introduces a novel privacy mask framework for securing network communication processes, specifically applied to consensus tracking. First, an innovative output mask map is presented to achieve time-selective privacy protection for dynamic network location data. The designed mask map overcomes key limitations of existing approaches, such as the inability to assign zero values to masked locations, the restriction of protection only to initial data, and the lack of rapid mask termination mechanisms. Second, the proposed framework is applied to multi-agent adaptive consensus tracking control to ensure the security of agents’ information and the boundedness of all signals in the closed-loop system. To counteract potential tracking performance degradation due to the computational complexity introduced by the privacy mask, prescribed-performance control theory is incorporated. Third, the dynamic surface control technique is used to avoid the derivation of the received encrypted position information, so as to handle the uncertainty originating from the unknown mask map. Ultimately, simulations comparing our framework with existing mask mechanisms highlight its advantages.