A Study of Check-in Privacy Protection in Social Networks

Abstract

The rapid development of social networks such as Foursquare Instagram Twitter Facebook has led to a significant increase in users of location-based services (LBS) These social networks allow users to check-in at the place they have visited and interact with others However recent researches show that the traditional check-in mechanism does not consider user’s social privacy problem adversary can easily infer user’s social relationship with others based on their check-in history data So that we introduce a novel problem in social network privacy protection research called Check-in Shielding against Acquaintance Inference (CSAI) the goal is to reduce user’s privacy risk by suggesting secure locations for user to perform check-in To address the CSAI problem we devise a check-in shielding framework called Check-in Shielding Scheme (CSS) which consist of two steps: quantify the social strength between users and recommend low privacy risk check-in locations for users We conducted experiment with two real-world datasets and the result show that CSS can effectively reduce the users’ acquaintances privacy risk and it is the best shielding method compared to other competitors under various experiment scenarios In addition CSS also can preserve the check-in distance of recommended place within reasonable range such that the usability of check-in data can be preserved

Date of Award	2019
Original language	English
Supervisor	Kun-Ta Chuang (Supervisor)