  We propose a new dynamic average consensus algorithm that is robust to
information-sharing noise arising from differential-privacy design. 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. We propose a new dynamic average consensus algorithm that
is robust to persistent and independent information-sharing noise added for the
purpose of differential-privacy protection. In fact, the algorithm 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), which, to our knowledge, has not been achieved before in average
consensus algorithms. Given that channel noise in communication can be viewed
as a special case of differential-privacy noise, the algorithm can also be used
to counteract communication imperfections. Numerical simulation results confirm
the effectiveness of the proposed approach.
