A Privacy Preserving Technique to Prevent Sensitive Behavior Exposure in Semantic Location-based Service

The increasing number of mobile device users indicates the expansion of personalized location-based services (LBS). Despite their proliferation, the risk of violating users’ privacy by exposing user's location information remains. For this reason, many studies have researched to prevent privacy violation in LBS. However, previous researches only focused on protecting users’ location information without considering semantic location privacy violation through contextual information. In this paper, we explain the process of inferring a user's behavior using semantic information which includes spatial and temporal information. We also suggest a privacy preserving technique to prevent exposure of sensitive behavior in semantic LBS. We implement an android application to validate the proposed technique. In accordance with the experimental results, the proposed b-diversity technique is validated to prevent exposure of sensitive behavior and also minimizing data utilization degradation.