Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties

This work develops a novel distributed differentially private protocol which naturally scales with the number of parties and analyzes the differential privacy guarantees of the protocol and the impact of the graph topology, showing that it can match the accuracy of the trusted curator model even when each party communicates with only a logarithmic number of other parties chosen at random.