Using location semantics to realize personalized road network location privacy protection

With the rapid development of location-based services in the field of mobile network applications, users enjoy the convenience of location-based services on one side, and they are exposed to the risk of privacy disclosure on the other side. Attackers may attack based on the semantic of the user’s location and user’s query location. A few of existing works on location privacy protection consider to protect the user’s location and his query location simultaneously, while the query location may reflect his requirement. In this paper, based on the existing location privacy protection framework, we first generate sensitive weight documents based on the user’s sensitivity to different location semantics automatically, then obtain the best collaborative segment for k-anonymity of the user’s location by using the reinforcement learning algorithm, and finally, the bidirectional k-disturbance of the user’s location and query location is performed based on the location semantics in real road network environment. The experiment verifies the effectiveness of the proposed method.