Title: Active or inactive: infer private user information in location-based social network

Authors: Guo Chi; Luo Meng; Liu Xuan; Cui Jingsong

Addresses: Global Navigation Satellite System Research Centerz Wuhan University, Wuhan, 430072, China ' Computer School, Wuhan University, Wuhan, 430072, China ' School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430072, China ' Computer School, Wuhan University, Wuhan, 430072, China

Abstract: Private user information can be compromised while revealing individual location data in widely used location-based social networks (LBSNs). In order to reveal the risk of location privacy faced by users, we demonstrate a method, which transforms social networks into Bayesian networks, to infer private information through the location data and relationships among users in LBSNs, such as Gowalla, regardless of whether users are active or inactive. Location data from active users can be easily used to infer private information like consumption level. For example, people who frequently appear in expensive restaurants are likely to rank the high consumption level. Those inactive users, who share sparse location data, reveal their private information through their active friends whose private information is easily divulged. Our experimental results show that friends have a high probability of having been to the same places. Combining with relationship data, the possibility of revealing private information is dramatically improved.

Keywords: location-based services; LBS; privacy inference; social networks; Bayesian networks; big data; private information.

DOI: 10.1504/IJES.2016.076112

International Journal of Embedded Systems, 2016 Vol.8 No.2/3, pp.185 - 195

Received: 17 Sep 2014
Accepted: 06 Nov 2014
Published online: 26 Apr 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article