From location to location pattern privacy in location-based services

Location privacy is extensively studied in the context of location-based services (LBSs). Typically, users are assigned a location privacy profile and the precise locations are cloaked so that the privacy profile is not compromised. Though being well-defined for snapshot location privacy, these solutions require additional precautions and patches in case of consecutive LBS requests on the user trajectory. The attacker can exploit some background knowledge like maximum velocity to compromise the privacy profile. To protect against this kind of location privacy attacks, PROBE (Damiani et al. in Trans Data Priv 3(2):123–148, 2010)-like systems constantly check location privacy violations and alter requests as necessary. Clearly, the location privacy is defined in terms of snapshot locations. Observing that there are usually user-specific movement patterns existing in the shared LBS requests, this work extends location privacy to location pattern privacy. We present a framework where user-specific sensitive movement patterns are defined and sanitized in offline and online fashions, respectively. Our solution uses an efficient dynamic programming approach to decide on and to prevent sensitive pattern disclosure. An extensive experimental evaluation has been carried out too.