TY - JOUR
T1 - A check-in shielding scheme against acquaintance inference in location-based social networks
AU - Chen, Bo Heng
AU - Li, Cheng Te
AU - Chuang, Kun Ta
N1 - Funding Information:
This work was supported in part by Ministry of Science and Technology, R.O.C., under Contract 107-2221-E-006-165-MY2, 107-2218-E-006-040, 107-2321-B-006-017, 107-2636-E-006-002 (MOST Young Scholar Fellowship) and 107-2221-E-006-199. In addition, we also thank the support from Academia Sinica under the Grant AS-107-TP-A05. Also, we would like to thank anonymous reviewers and the editor for their very useful comments and suggestions.
Publisher Copyright:
© 2018, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2019/11/1
Y1 - 2019/11/1
N2 - Location-based social services such as Foursquare and Facebook Place allow users to perform check-ins at places and interact with each other in geography (e.g. check-in together). While existing studies have exhibited that the adversary can accurately infer social ties based on check-in data, the traditional check-in mechanism cannot protect the acquaintance privacy of users. In this work, therefore, we propose a novel shielding check-in system, whose goal is to guide users to check-in at secure places. We accordingly propose a novel research problem, Check-in Shielding against Acquaintance Inference (CSAI), which aims at recommending a list of secure places when users intend to check-ins so that the potential that the adversary correctly identifies the friends of users can be significantly reduced. We develop the Check-in Shielding Scheme (CSS) framework to solve the CSAI problem. CSS consists of two steps, namely estimating the social strength between users and generating a list of secure places. Experiments conducted on Foursquare and Gowalla check-in datasets show that CSS is able to not only outperform several competing methods under various scenario settings, but also lead to the check-in distance preserving and ensure the usability of the new check-in data in Point-of-Interest (POI) recommendation.
AB - Location-based social services such as Foursquare and Facebook Place allow users to perform check-ins at places and interact with each other in geography (e.g. check-in together). While existing studies have exhibited that the adversary can accurately infer social ties based on check-in data, the traditional check-in mechanism cannot protect the acquaintance privacy of users. In this work, therefore, we propose a novel shielding check-in system, whose goal is to guide users to check-in at secure places. We accordingly propose a novel research problem, Check-in Shielding against Acquaintance Inference (CSAI), which aims at recommending a list of secure places when users intend to check-ins so that the potential that the adversary correctly identifies the friends of users can be significantly reduced. We develop the Check-in Shielding Scheme (CSS) framework to solve the CSAI problem. CSS consists of two steps, namely estimating the social strength between users and generating a list of secure places. Experiments conducted on Foursquare and Gowalla check-in datasets show that CSS is able to not only outperform several competing methods under various scenario settings, but also lead to the check-in distance preserving and ensure the usability of the new check-in data in Point-of-Interest (POI) recommendation.
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U2 - 10.1007/s11280-018-0653-3
DO - 10.1007/s11280-018-0653-3
M3 - Article
AN - SCOPUS:85058424234
SN - 1386-145X
VL - 22
SP - 2321
EP - 2354
JO - World Wide Web
JF - World Wide Web
IS - 6
ER -