Abstract
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.
| Original language | English |
|---|---|
| Journal | World Wide Web |
| DOIs | |
| Publication status | Accepted/In press - 2018 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Hardware and Architecture
- Computer Networks and Communications
Cite this
}
A check-in shielding scheme against acquaintance inference in location-based social networks. / Chen, Bo Heng; Li, Cheng-Te; Chuang, Kun-Ta.
In: World Wide Web, 01.01.2018.Research output: Contribution to journal › Article
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
PY - 2018/1/1
Y1 - 2018/1/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85058424234&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058424234&partnerID=8YFLogxK
U2 - 10.1007/s11280-018-0653-3
DO - 10.1007/s11280-018-0653-3
M3 - Article
AN - SCOPUS:85058424234
JO - World Wide Web
JF - World Wide Web
SN - 1386-145X
ER -