Differentially Private Iterative Synchronous Consensus
This addresses privacy concerns in distributed coordination systems like load balancing and autonomous vehicles, but is incremental as it adapts existing differential privacy to iterative consensus.
The paper tackles the problem of achieving consensus among distributed agents while protecting the privacy of their initial values from adversaries, introducing differentially private mechanisms and establishing a tradeoff between privacy and accuracy.
The iterative consensus problem requires a set of processes or agents with different initial values, to interact and update their states to eventually converge to a common value. Protocols solving iterative consensus serve as building blocks in a variety of systems where distributed coordination is required for load balancing, data aggregation, sensor fusion, filtering, clock synchronization and platooning of autonomous vehicles. In this paper, we introduce the private iterative consensus problem where agents are required to converge while protecting the privacy of their initial values from honest but curious adversaries. Protecting the initial states, in many applications, suffice to protect all subsequent states of the individual participants. First, we adapt the notion of differential privacy in this setting of iterative computation. Next, we present a server-based and a completely distributed randomized mechanism for solving private iterative consensus with adversaries who can observe the messages as well as the internal states of the server and a subset of the clients. Finally, we establish the tradeoff between privacy and the accuracy of the proposed randomized mechanism.