Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks
This work addresses privacy-preserving mean estimation for distributed systems in unreliable networks, such as IoT or wireless sensor networks, offering a solution with both implicit and explicit privacy mechanisms, though it appears incremental as it builds on existing collaborative and differential privacy methods.
The paper tackles the problem of privately estimating the mean of vectors across nodes in an unreliable wireless network with intermittent connections, proposing PriCER, a differentially private collaborative algorithm that optimizes the tradeoff between collaboration and privacy leakage, achieving theoretical privacy guarantees and validated through numerical simulations.
We consider the problem of privately estimating the mean of vectors distributed across different nodes of an unreliable wireless network, where communications between nodes can fail intermittently. We adopt a semi-decentralized setup, wherein to mitigate the impact of intermittently connected links, nodes can collaborate with their neighbors to compute a local consensus, which they relay to a central server. In such a setting, the communications between any pair of nodes must ensure that the privacy of the nodes is rigorously maintained to prevent unauthorized information leakage. We study the tradeoff between collaborative relaying and privacy leakage due to the data sharing among nodes and, subsequently, propose PriCER: Private Collaborative Estimation via Relaying -- a differentially private collaborative algorithm for mean estimation to optimize this tradeoff. The privacy guarantees of PriCER arise (i) implicitly, by exploiting the inherent stochasticity of the flaky network connections, and (ii) explicitly, by adding Gaussian perturbations to the estimates exchanged by the nodes. Local and central privacy guarantees are provided against eavesdroppers who can observe different signals, such as the communications amongst nodes during local consensus and (possibly multiple) transmissions from the relays to the central server. We substantiate our theoretical findings with numerical simulations. Our implementation is available at https://github.com/rajarshisaha95/private-collaborative-relaying.