A differentially k-anonymity-based location privacy-preserving for mobile crowdsourcing systems

With the rapid development of mobile devices, the problem privacy leaking has become an important research focus in the field of mobile crowdsourcing. In order to guarantee the security and truthfulness of mobile crowdsourcing, this paper proposes a differentially k-anonymity location privacy-preserving for mobile crowdsourcing. Through combining k-anonymity and differential privacy-preserving, the differentially k-anonymity-based location privacy-preserving is proposed in order to prevent workers' location information from being leaked. Through comparison experiments, the effectiveness, adaptation and flexibility of the proposed differentially k-anonymity-based location privacy-preserving is verified. The differentially k-anonymity-based location privacy preserving can inspire workers to participate crowd tasks, and protect workers' location privacy effectively. Copyright (C) 2018 Elsevier Ltd. All rights reserved.