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Location Privacy Context Information Effects Using Bayesian Inference Framework

Abstract

Smartphones, among other increasingly powerful mobile computing devices, offer various methods of localization. Integrated GPS receivers, or positioning services based on nearby communication infrastructure (Wi-Fi access points or base stations of cellular networks), enable users to position themselves fairly accurately, which has led to a wide offering of Location-based Services (LBSs). Such services can be queried by users to provide real-time information related to the current position and surroundings of the device, e.g., contextual data about points of interest such as petrol stations, or more dynamic information such as traffic conditions. The value of LBSs is in their ability to obtain on the fly up-to-date information. Although LBSs are convenient, disclosing location information can be dangerous. Each time an LBS query is submitted private information is revealed. Users can be linked to their locations, and multiple pieces of such information can be linked together. They can then be profiled, which leads to unsolicited targeted advertisements or price discrimination. Even worse, the habits, personal and private preferences, religious beliefs, and political affiliations, for example, can be inferred from a user’s whereabouts. This could make her the target of blackmail or harassment. Finally, real-time location disclosure leaves a person vulnerable to absence disclosure attacks: learning that someone is away from home could enable someone to break into her house or blackmail her [1]. An stalker can also exploit the location information.

B.Ram Kumar s Kripa Sankar Prof.K.Ravikumar

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