Toward privacy-preserving diffusion strategies for adaptation and learning over networks

Distributed optimization allows to address inference problems in a decentralized manner over networks, where agents can exchange information with their neighbors to improve their local estimates. Privacy preservation has become an important issue in many data mining applications. It aims at protecting the privacy of individual data in order to prevent the disclosure of sensitive information during the learning process. In this paper, we derive a diffusion strategy of the LMS type to solve distributed inference problems in the case where agents are also interested in preserving the privacy of the local measurements. We carry out a detailed mean and mean-square error analysis of the algorithm. Simulations are provided to check the theoretical findings.