Differentially Private Distributed Optimization

Understand

In distributed optimization and iterative consensus literature, a standard problem is for $N$ agents to minimize a function $f$ over a subset of Euclidean space, where the cost function is expressed as a sum $\sum f_i$.

• In this paper, we study the private distributed optimization (PDOP) problem with the additional requirement that the cost function of the individual agents should remain differentially private.
• The adversary attempts to infer information about the private cost functions from the messages that the agents exchange.
• Achieving differential privacy requires that any change of an individual's cost function only results in unsubstantial changes in the statistics of the messages.