Privacy-Aware Information Sharing in Location-Based Services: Attacks and Defense

Zhikai XU; Hongli ZHANG; Xiangzhan YU; Shen SU

doi:10.1587/transinf.2015INP0001

Privacy-Aware Information Sharing in Location-Based Services: Attacks and Defense

Zhikai XU, Hongli ZHANG, Xiangzhan YU, Shen SU

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Location-based services (LBSs) are useful for many applications in internet of things(IoT). However, LBSs has raised serious concerns about users' location privacy. In this paper, we propose a new location privacy attack in LBSs called hidden location inference attack, in which the adversary infers users' hidden locations based on the users' check-in histories. We discover three factors that influence individual check-in behaviors: geographic information, human mobility patterns and user preferences. We first separately evaluate the effects of each of these three factors on users' check-in behaviors. Next, we propose a novel algorithm that integrates the above heterogeneous factors and captures the probability of hidden location privacy leakage. Then, we design a novel privacy alert framework to warn users when their sharing behavior does not match their sharing rules. Finally, we use our experimental results to demonstrate the validity and practicality of the proposed strategy.

Publication: IEICE TRANSACTIONS on Information Vol.E99-D No.8 pp.1991-2001

Publication Date: 2016/08/01

Publicized: 2016/05/31

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2015INP0001

Type of Manuscript: Special Section PAPER (Special Section on Security, Privacy and Anonymity of Internet of Things)

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Authors

Zhikai XU
  Harbin Institute of Technology
Hongli ZHANG
  Harbin Institute of Technology
Xiangzhan YU
  Harbin Institute of Technology
Shen SU
  Harbin Institute of Technology

Keyword

location privacy, inference attack, privacy enhancing technology, location-based services, internet of things

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Zhikai XU, Hongli ZHANG, Xiangzhan YU, Shen SU, "Privacy-Aware Information Sharing in Location-Based Services: Attacks and Defense" in IEICE TRANSACTIONS on Information, vol. E99-D, no. 8, pp. 1991-2001, August 2016, doi: 10.1587/transinf.2015INP0001.
Abstract: Location-based services (LBSs) are useful for many applications in internet of things(IoT). However, LBSs has raised serious concerns about users' location privacy. In this paper, we propose a new location privacy attack in LBSs called hidden location inference attack, in which the adversary infers users' hidden locations based on the users' check-in histories. We discover three factors that influence individual check-in behaviors: geographic information, human mobility patterns and user preferences. We first separately evaluate the effects of each of these three factors on users' check-in behaviors. Next, we propose a novel algorithm that integrates the above heterogeneous factors and captures the probability of hidden location privacy leakage. Then, we design a novel privacy alert framework to warn users when their sharing behavior does not match their sharing rules. Finally, we use our experimental results to demonstrate the validity and practicality of the proposed strategy.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2015INP0001/_p

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@ARTICLE{e99-d_8_1991,
author={Zhikai XU, Hongli ZHANG, Xiangzhan YU, Shen SU, },
journal={IEICE TRANSACTIONS on Information},
title={Privacy-Aware Information Sharing in Location-Based Services: Attacks and Defense},
year={2016},
volume={E99-D},
number={8},
pages={1991-2001},
abstract={Location-based services (LBSs) are useful for many applications in internet of things(IoT). However, LBSs has raised serious concerns about users' location privacy. In this paper, we propose a new location privacy attack in LBSs called hidden location inference attack, in which the adversary infers users' hidden locations based on the users' check-in histories. We discover three factors that influence individual check-in behaviors: geographic information, human mobility patterns and user preferences. We first separately evaluate the effects of each of these three factors on users' check-in behaviors. Next, we propose a novel algorithm that integrates the above heterogeneous factors and captures the probability of hidden location privacy leakage. Then, we design a novel privacy alert framework to warn users when their sharing behavior does not match their sharing rules. Finally, we use our experimental results to demonstrate the validity and practicality of the proposed strategy.},
keywords={},
doi={10.1587/transinf.2015INP0001},
ISSN={1745-1361},
month={August},}

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TY - JOUR
TI - Privacy-Aware Information Sharing in Location-Based Services: Attacks and Defense
T2 - IEICE TRANSACTIONS on Information
SP - 1991
EP - 2001
AU - Zhikai XU
AU - Hongli ZHANG
AU - Xiangzhan YU
AU - Shen SU
PY - 2016
DO - 10.1587/transinf.2015INP0001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2016
AB - Location-based services (LBSs) are useful for many applications in internet of things(IoT). However, LBSs has raised serious concerns about users' location privacy. In this paper, we propose a new location privacy attack in LBSs called hidden location inference attack, in which the adversary infers users' hidden locations based on the users' check-in histories. We discover three factors that influence individual check-in behaviors: geographic information, human mobility patterns and user preferences. We first separately evaluate the effects of each of these three factors on users' check-in behaviors. Next, we propose a novel algorithm that integrates the above heterogeneous factors and captures the probability of hidden location privacy leakage. Then, we design a novel privacy alert framework to warn users when their sharing behavior does not match their sharing rules. Finally, we use our experimental results to demonstrate the validity and practicality of the proposed strategy.
ER -