Learning information release policies for preventing intersection attacks

Location Based Services in the world of mobile devices has been used widely in all sectors and in various applications. It allows one to track the location, track services and also perfom other location based operations. Although it's uses are very significant, the information related to location has opened up opportunities for adversary to misuse the mobile clients. This paper addresses one such problem with location information release and the problem addressed is mainly around Intersection Attacks. The two geo-spatial information over a period of time may be kept private but the time interval between the two specific time periods could lead to information leakage. This paper proposes two methodologies called “hierarchical signatures” and “Markov devision process” to mitigate intersection attacks. The paper also details the experimentation and results based on the tests done on two types of datasetc including reality mining datasets and telecommunication datasets.