Privacy-Enhancing Preferential LBS Query for Mobile Social Network Users

• Published in: Wireless communications and mobile computing 2020, Vol.2020 (2020), p.1-13
• Main Authors: Tian, Zhi, Cheng, Xiuzhen, Li, Yingshu, Siddula, Madhuri, Cai, Zhipeng
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Ambient intelligence; Coffeehouses; Location based services; Methods; Privacy; Queries; Recommender systems; Restaurants; Social networks; User profiles; Wireless networks
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2020/8892321

While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through k-anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.