Open AccessThe Novel Location Privacy-Preserving CKD for Mobile Crowdsourcing Systems
Author(s) -
Zhongyang Chi,
Yingjie Wang,
Yan Huang,
Xiangrong Tong
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2017.2783322
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.
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