Privacy-preserving collaborative deep learning with application to human activity recognition

Abstract

The proliferation of wearable devices has contributed to the emergence of mobile crowdsensing, which leverages the power of the crowd to collect and report data to a third party for large-scale sensing and collaborative learning. However, since the third party may not be honest, privacy poses a major concern. In this paper, we address this concern with a two-stage privacy-preserving scheme called RG-RP: the first stage is designed to mitigate maximum a posteriori (MAP) estimation attacks by perturbing each participant's data through a nonlinear function called repeated Gompertz (RG); while the second stage aims to maintain accuracy and reduce transmission energy by projecting high-dimensiona..