ILLIA: Enabling $k$ -Anonymity-Based Privacy Preserving Against Location Injection Attacks in Continuous LBS Queries

With the increasing popularity of location-based services (LBSs), it is of paramount importance to preserve one’s location privacy. The commonly used location privacy preserving approach, location
${k}$
-anonymity, strives to aggregate the queries of
${k}$
nearby users within a so-called cloaked region via a trusted third-party anonymizer. As such, the probability to identify the location of every user involved is no more than
${1/k}$
, thus offering privacy preservation for users. One inherent limitation of
${k}$
-anonymity, however, is that all users involved are assumed to be trusted and report their real locations. When location injection attacks (LIAs) are conducted, where the untrusted users inject fake locations (along with fake queries) to the anonymizer, the probability of disclosing one’s location privacy could be greatly more than
${1/k}$
, yielding a much higher risk of privacy leakage. To tackle this problem, in this paper we present ILLIA, the first work that enables
${k}$
-anonymity-based privacy preservation against LIA in continuous LBS queries. Central to the ILLIA idea is to explore the pattern of the users’ mobility in continuous LBS queries. With a thorough understanding of the users’ mobility similarity, a credibility-based
${k}$
-anonymity scheme is developed, such that ILLIA is able to defense against LIA without requiring in advance knowledge of how fake locations are manipulated while still maintaining high quality of services. Both the effectiveness and the efficiency of ILLIA are validated by extensive simulations on real world dataset loc-Gowalla.