A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion
arXiv:1901.09367v111 citations
AI Analysis
This work addresses privacy concerns in distributed computing for applications like sensor networks, but it appears incremental as it builds on existing gossip algorithms with added noise mechanisms.
The paper tackles the average consensus problem with privacy protection for nodes' initial values by introducing a randomized gossip algorithm with controlled noise insertion, providing iteration complexity bounds and numerical validation.
In this work we present a randomized gossip algorithm for solving the average consensus problem while at the same time protecting the information about the initial private values stored at the nodes. We give iteration complexity bounds for the method and perform extensive numerical experiments.