Search

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.

We in this paper explore a new link prediction paradigm, called ‘worship’ prediction, to discover worship links between users and celebrities on social networks. The prediction of ‘worship’ links enables valuable social services, such as viral marketing, popularity estimation, and celebrity recommendation. However, as the concern of business security and personal privacy, only public-accessible statistical social properties, instead of the detailed information of users, can be utilized to predict the ‘worship’ labels. In addition, we observe that friendship properties are not effective to predict the desired links, meaning that most of previous work which rely on the friendship properties cannot be successfully applied in the prediction of worship link. To address these issues, a novel learning framework is devised, including a factor graph with new discovered statistical properties and a Gaussian estimation based learning algorithm with active learning. Our experimental studies on real data, including Instagram, Twitter and DBLP, show that the proposed learning framework can overcome the problem of missing labels and efficiently discover worship links.