Abstract
The fusion of mobile devices and social networks is stimulating a wider use of Location Based Service (LBS) and makes it become an important part in our daily life. However, the problem of privacy leakage has become a main factor that hinders the further development of LBS. When a LBS user sends queries to the LBS server, the user’s personal privacy in terms of identity and location may be leaked to the attacker. To protect user’s privacy, Niu et al. proposed an algorithm named enhanced-Dummy Location Selection (en-DLS). In this paper, we introduce two attacks to en-DLS, namely long-term statistical attack (LSA) and regional statistical attack (RSA). In the proposed attacks, an attacker can obtain the privacy contents of a user by analyzing LBS historical data, which causes en-DLS to be invalid for user’s privacy protection. Furthermore, this paper proposes a set of privacy protection schemes against both LSA and RSA. For LSA, we propose two protection methods named multiple user name (MNAME) and same user name (SNAME). To solve the regional privacy issue, we divide the map into various regions with different requirements on privacy protection. For this purpose, four levels of protection requirements (PLs) are defined, and true location is protected by allocating a certain number of positions from the dummies according to the location’s PL. Performance analysis and simulation results show that our proposed methods can completely avoid the vulnerabilities of en-DLS to both LSA and RSA, and incur marginal increase of communication overhead and computational cost.
•According to the rule of activity of most users, we introduce LSA by using the historical data. For the attack, we give out two methods to preserve user’s privacy.•We divide the regions in the map into different PLs according to the privacy requirement. We design algorithm to make the regions of high level to be dummies at a high rate and the regions of low level at a low rate. The problem that the attacker can violate the privacy of a particular region by analyzing the historical data is solved.•We analyze the ability to preserve user’s privacy by entropy. The internal relation among the frequency of user’s LBS query, the division of regions in the map, and the length of the interval of historical information collected is discussed.