A framework for Enhancing Privacy in Location Based Services

Abstract

Recently anonymity in location based services has attracted a great deal of attention. This is due to the emerging location-detection devices together with ubiquitous connectivity that have enabled a large variety of location based services. Users have to reveal their location information to access location based services. However, this may threaten the user‟s privacy. Different solution approaches have been proposed to tackle this problem. K-anonymity approach has been studied extensively in various forms, however it is only effective when the user location is fixed. When a user moves and continuously sends the rectangular regions containing her location to the Location Service Provider (LSP), the LSP can still approximate the user‟s trajectory if it takes into account the overlap of consecutive rectangles, which poses a threat to the trajectory privacy of the user. This study reviewed recent advancement for offering K-anonymity and the most prevalent methodology. The outcome of the review is a framework that enhances privacy in Location Based Services. This framework ensures that user privacy is enhanced for both snapshot and continuous queries. The efficiency and effectiveness of the proposed framework was evaluated and the results indicates that the proposed framework has high success rate and good run time performance.