Algorithm-Level Confidentiality for Average Consensus on Time-Varying Directed Graphs
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Average consensus plays a key role in distributed networks, with applications ranging from time synchronization, information fusion, load balancing, to decentralized control. Existing average consensus algorithms require individual agents to exchange explicit state values with their neighbors, which leads to the undesirable disclosure of sensitive information in the state. In this paper, we propose a novel average consensus algorithm for time-varying directed graphs that can protect the confidentiality of a participating agent against other participating agents. The algorithm injects randomness in interaction to obfuscate information on the algorithm-level and can ensure information-theoretic privacy without the assistance of any trusted third party or data aggregator. By leveraging the inherent robustness of consensus dynamics against random variations in interaction, our proposed algorithm can also guarantee the accuracy of average consensus. The algorithm is distinctly different from differential-privacy based average consensus approaches which enable confidentiality through compromising accuracy in obtained consensus value. Numerical simulations confirm the effectiveness and efficiency of our proposed approach.
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