We address differential privacy for dynamic average consensus. Not only is dynamic average consensus widely used in cooperative control and distributed tracking, it is also a fundamental building block in numerous distributed computation algorithms such as multi-agent optimization and distributed Nash equilibrium seeking. By co-designing the dynamic average consensus mechanism and the differential-privacy noise injection mechanism, we propose the first dynamic average consensus algorithm that can ensure both provable convergence to the exact average reference signal and rigorous $\epsilon$-differential privacy, even when the number of iterations tends to infinity. Given that dynamic average consensus includes the static average consensus as a special case, the approach can also be used to ensure rigorous $\epsilon$-differential privacy in static average consensus while maintaining accurate consensus result. To our knowledge, ensuring both provable convergence and rigorous $\epsilon$-differential privacy (even for infinite number of iterations) has not been achieved before in average consensus algorithms. Numerical simulation results confirm the effectiveness of the proposed approach.

翻译：我们处理动态平均共识的差异。不仅是在合作控制和分布跟踪中广泛使用的动态平均共识,它也是多种分布式计算算法(如多剂优化和分布式纳什均衡)中的基本构件。通过共同设计动态平均共识机制和差异隐私注射机制,我们建议采用第一种动态平均共识算法,既能确保与准确平均参考信号的可证实一致,又能确保严格的美元差异隐私(即使是无限的重复)在平均共识算法之前就尚未实现。数字模拟结果证实了拟议方法的有效性。