Mobility-Aware Prefetching and Replacement Scheme for Location-Based Services

Abstract

Location-based services (LBS) are gaining prominence in today's environment. When a mobile user submits a location-based query in LBS, an adversary may infer the locations or other related sensitive information. Thus, an efficient location privacy preservation model (LPPM) with minimal overhead needs to be built by considering contextual understanding and analytical ability. With consideration of service efficiency and privacy, a location privacy preservation policy, namely mobility-aware prefetching and replacement (MOPAR) policy, has been proposed by the cloaking area formulation through user location, cache contribution rate, and data freshness in LBS. An incorporation of prefetching and replacement to anonymizer and consumer cache with formulation of cloak area is being deployed to protect customer sensitive information. The Markov mobility model-based next-position prediction procedure is used in this chapter for caching and formulation of cloaking area. The results of the simulation show significant enhancement in the efficiency of the location-privacy preservation model.