دانلود رایگان مقاله انگلیسی شناسایی نزدیکی موثر و حفظ حریم خصوصی در برنامه های اجتماعی - IEEE 2018

Abstract

With the pervasiveness of location-aware mobile terminals and the popularity of social applications, location-based social networking service (LBSNS) has brought great convenience to people’s life. Meanwhile, proximity detection, which makes LBSNS more flexible, has aroused widespread concern. However, the prosperity of LBSNS still faces many severe challenges on account of users’ location privacy and data security. In this paper, we propose two efficient and privacy-preserving proximity detection schemes, named AGRQ-P and AGRQ-C, for locationbased social applications. With proposed schemes, a user can choose any area on the map, and query whether her/his friends are within the region without divulging the query information to both social application servers and other users, meanwhile, the accurate locations of her/his friends are also confidential for the servers and the query user. Specifically, with algorithms based on ciphertext of geometric range query, users’ query and location information is blurred into chipertext in client, thus no one but the user knows her/his own sensitive information. Detailed security analysis shows that various security threats can be defended. In addition, the proposed schemes are implemented in an IM APP with a real LBS dataset, and extensive simulation results over smart phones further demonstrate that AGRQ-P and AGRQ-C are highly efficient and can be implemented effectively.

Conclusion

In this paper, we have proposed two secure, efficient, and privacy-preserving proximity detection schemes for social applications, called AGRQ-P and AGRQ-C, which proposed new methods for arbitrary geometric range query with improved privacy-preserving cosine similarity computing protocol and point in polygon strategies. The proposed schemes can provide accurate proximity detection results without divulging a user’s query and accurate location information to both social application servers and other users. Detailed security analysis shows their security strength and privacy-preserving ability, and extensive experiments are conducted to demonstrate their efficiencies.