Privacy-Enhancing Preferential LBS Query for Mobile Social Network Users

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 syst...

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Bibliographic Details
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
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Summary: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.
ISSN:1530-8669
1530-8677
DOI:10.1155/2020/8892321