The Novel Location Privacy-Preserving CKD for Mobile Crowdsourcing Systems

Abstract

With the development of mobile devices, mobile crowdsourcing has become the research hotspot in mobile crowd sensing networks (MCSS). How to protect the location privacy of mobile user in location-based services is a key problem in MCSS. However, with the increase of privacy-preserving level, the service quality will be influenced and decrease. In order to prevent mobile user's location privacy from being leaked, this paper proposes a location privacy-preserving mechanism CKD through combining k-anonymity and differential privacy-preserving. In addition, the tradeoff between privacy protection and service quality is solved based on Stackelberg game. Through comparison experiments, the proposed location privacy-preserving CKD is verified. In addition, the tradeoff between privacy protection and service quality can be solved by our location privacy-preserving.

Keywords

• Mobile crowdsourcing
• MCSS
• location privacy
• k-anonymity%22%2C%22default_operator%22%3A%22AND%22%7D%7D%7D"> <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-anonymity
• differential privacy-preserving