We propose a location-based query anonymization technique, LBS (k,T)-anonymization, that ensures anonymity of user's query in a specific time window against what we call known user attack. We distinguish between our technique and related work on k-anonymity for LBSs by showing that they target different privacy inference attacks. Also, we analyze the inconsistency of the existing predominant approach with the original definition of k-anonymity and its implications on the anonymization. Finally, we present an evaluation framework that assess the applicability and performance of the proposed technique using an evaluation framework. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications-Spatial databases and GIS; K.4.1 [Computers and Society]: Public Policy Issues--Privacy General Terms Algorithms, Security Keywords privacy, anonymity, k-anonymity, LBS
Amirreza Masoumzadeh, James Joshi, Hassan A. Karim