TY - GEN
T1 - Optimizing location quality in privacy preserving crowdsensing
AU - Zhang, Yuhui
AU - Li, Ming
AU - Yang, Dejun
AU - Tang, Jian
AU - Xue, Guoliang
N1 - Funding Information:
Zhang and Li are affiliated with Colorado School of Mines, Golden, CO 80401. Yang (corresponding author) is with Colorado School of Mines, Golden, CO 80401 USA and Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023 China. Tang is affiliated with Syracuse University, Syracuse, NY 13244. Xue is affiliated with Arizona State University, Tempe, AZ 85287. Email:{yuhzhang, mili, djyang}@mines.edu, jtang02@syr.edu, xue@asu.edu. This research was supported in part by NSF grants 1525920, 1704092, 1717197, and 1717315. The information reported here does not reflect the position or the policy of the federal government.
PY - 2019/12
Y1 - 2019/12
N2 - Crowdsensing enables a wide range of data collection, where the data are usually tagged with private locations Protecting users' location privacy has been a central issue. The study of various location perturbation techniques for protecting users' location privacy has received widespread attention. Despite the huge promise and considerable attention, the location perturbation operation causes inevitable location errors, which can diminish the location quality of the crowdsensing results. Provable good algorithms that consider location quality in privacy preserving crowdsensing from optimization perspectives are still lacking in the literature. In this paper, we investigate the problem of location quality optimization in privacy preserving crowdsensing, which is to minimize the location quality desegregation, while protecting all users' location privacy. We present an optimal algorithm OLQDM for this problem. Extensive simulations demonstrate that OLQDM significantly outperforms an existing algorithm in terms of the location quality and SSE.
AB - Crowdsensing enables a wide range of data collection, where the data are usually tagged with private locations Protecting users' location privacy has been a central issue. The study of various location perturbation techniques for protecting users' location privacy has received widespread attention. Despite the huge promise and considerable attention, the location perturbation operation causes inevitable location errors, which can diminish the location quality of the crowdsensing results. Provable good algorithms that consider location quality in privacy preserving crowdsensing from optimization perspectives are still lacking in the literature. In this paper, we investigate the problem of location quality optimization in privacy preserving crowdsensing, which is to minimize the location quality desegregation, while protecting all users' location privacy. We present an optimal algorithm OLQDM for this problem. Extensive simulations demonstrate that OLQDM significantly outperforms an existing algorithm in terms of the location quality and SSE.
UR - http://www.scopus.com/inward/record.url?scp=85081947498&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081947498&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM38437.2019.9014089
DO - 10.1109/GLOBECOM38437.2019.9014089
M3 - Conference contribution
AN - SCOPUS:85081947498
T3 - 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
BT - 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Global Communications Conference, GLOBECOM 2019
Y2 - 9 December 2019 through 13 December 2019
ER -