ABSTRACT

It is vital to manage mobile users’ privacy and quality of service in location aided services and applications. Today’s architectures and model are all commercial oriented, which underweights the privacy and highlights the quality of services. It results in loss and misuse of privacy. Different authors have proposed architectures and methods to protect location privacy. These architectures are three tier, multi-tier, centralised, distributed. We have proposed peer to peer, cooperative broadcast users model to protect mobile users privacy. Our motivation lies in the natural human process of enquiry. When user reaches to unfamiliar area, user enquires the local user about point of interests. Local user is persistent in that area and has lot of knowledge of that area. Mobile user just asks query to local user and does reveal about his identity. So all users are persistent and expert in their local areas or areas where they spend more and more time. Technically, every user stores in its mobile device points of interests. This database is his knowledge of POIs in his persistent area. Requester user broadcasts his query, and other local user also broadcasts POI replies, if answer is in his database. In summary, this is a peer to peer, broadcast, client server, and distributed request-reply model. Next, POIs learning is done from government authorised, centralised trusted server, by the mobile users. So every mobile device has to study its own persistency and based on that information, download and maintain POIs in its data base system. So that user can cooperatively share whatever POI knowledge he has. We have designed such a system, implemented and analysed such system. We have also analysed the privacy, performance, persistency optimization in this work. We found that this mechanism is stronger to protect the location privacy but it is a challenge to address cooperative nature of human being and commercial aspect of service providers.