Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy
This work addresses privacy-preserving data aggregation in unreliable networks, which is incremental as it builds on existing distributed and private estimation methods.
The paper tackles the problem of distributed mean estimation over networks with intermittent connectivity while ensuring peer-to-peer privacy, proposing a novel algorithm that achieves an optimal tradeoff between collaboration and privacy leakage.
This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central server. To mitigate the impact of intermittent links, nodes can collaborate with their neighbors to compute local consensus which they forward to the central server. In such a setup, the communications between any pair of nodes must satisfy local differential privacy constraints. We study the tradeoff between collaborative relaying and privacy leakage due to the additional data sharing among nodes and, subsequently, propose a novel differentially private collaborative algorithm for DME to achieve the optimal tradeoff. Finally, we present numerical simulations to substantiate our theoretical findings.