TY - JOUR
T1 - Tradeoff Between Location Quality and Privacy in Crowdsensing
T2 - An Optimization Perspective
AU - Zhang, Yuhui
AU - Li, Ming
AU - Yang, Dejun
AU - Tang, Jian
AU - Xue, Guoliang
AU - Xu, Jia
N1 - Funding Information:
Manuscript received September 27, 2019; revised December 18, 2019 and January 23, 2020; accepted January 26, 2020. Date of publication February 10, 2020; date of current version April 14, 2020. This work was supported in part by NSF under Grant 1525920, Grant 1704092, Grant 1717197, and Grant 1717315. This article was presented in part at 2019 IEEE GLOBECOM. (Corresponding author: Dejun Yang.) Yuhui Zhang, Ming Li, and Dejun Yang are with the Department of Computer Science, Colorado School of Mines, Golden, CO 80401 USA (e-mail: yuhzhang@mines.edu; mili@mines.edu; djyang@mines.edu).
Publisher Copyright:
© 2014 IEEE.
PY - 2020/4
Y1 - 2020/4
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, e.g., 'k' -anonymity, for location privacy has received widespread attention. Despite the huge promise and considerable attention, provable good algorithms considering the tradeoff between location privacy and location information quality from the optimization perspective in crowdsensing are lacking in the literature. In this article, we study two related optimization problems from two different perspectives. The first problem is to minimize the location quality degradation caused by the protection of users' location privacy. We present an efficient optimal algorithm OLoQ for this problem. The second problem is to maximize the number of protected users, subject to a location quality degradation constraint. To satisfy the different requirements of the platform, we consider two cases for this problem: 1) overlapping and 2) nonoverlapping perturbations. For the former case, we give an efficient optimal algorithm OPUMO. For the latter case, we first prove its NP-hardness. We then design a '(1-\epsilon)' -approximation algorithm NPUMN and a fast and effective heuristic algorithm HPUMN. Extensive simulations demonstrate that OLoQ, OPUMO, and HPUMN significantly outperform an existing algorithm.
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, e.g., 'k' -anonymity, for location privacy has received widespread attention. Despite the huge promise and considerable attention, provable good algorithms considering the tradeoff between location privacy and location information quality from the optimization perspective in crowdsensing are lacking in the literature. In this article, we study two related optimization problems from two different perspectives. The first problem is to minimize the location quality degradation caused by the protection of users' location privacy. We present an efficient optimal algorithm OLoQ for this problem. The second problem is to maximize the number of protected users, subject to a location quality degradation constraint. To satisfy the different requirements of the platform, we consider two cases for this problem: 1) overlapping and 2) nonoverlapping perturbations. For the former case, we give an efficient optimal algorithm OPUMO. For the latter case, we first prove its NP-hardness. We then design a '(1-\epsilon)' -approximation algorithm NPUMN and a fast and effective heuristic algorithm HPUMN. Extensive simulations demonstrate that OLoQ, OPUMO, and HPUMN significantly outperform an existing algorithm.
KW - Crowdsensing
KW - k-anonymity
KW - location data quality
KW - location privacy
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U2 - 10.1109/JIOT.2020.2972555
DO - 10.1109/JIOT.2020.2972555
M3 - Article
AN - SCOPUS:85083732421
SN - 2327-4662
VL - 7
SP - 3535
EP - 3544
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 4
M1 - 8988265
ER -