Privacy-preserving collaborative fuzzy clustering

Abstract

The proliferation of Internet of Things devices has contributed to the emergence of participatory sensing (PS), where multiple individuals collect and report their data to a third-party data mining cloud service for analysis. The need for the participants to collaborate with each other for this analysis gives rise to the concept of collaborative learning. However, the possibility of the cloud service being semi-honest poses a key challenge: preserving the participants’ privacy. In this paper, we address this challenge with a two-stage scheme called RG+RP: in the first stage, each participant perturbs his/her data by passing the data through a nonlinear function called repeated Gompertz (RG);..