L2P2: A location-label based approach for privacy preserving in LBS

The developments in positioning and mobile communication technology have made the location-based service (LBS) applications more and more popular. For privacy reasons and due to lack of trust in the LBS providers, k-anonymity and 1-diversity techniques have been widely used to preserve privacy of users in distributed LBS architectures in Internet of Things (IoT). However, in reality, there are scenarios where the locations of users are identical or similar/near each other in IoT. In such scenarios the k locations selected by k-anonymity technique are the same and location privacy can be easily compromised or leaked. To address the issue of privacy preservation, in this paper, we introduce the location labels to distinguish locations of mobile users to sensitive and ordinary locations. We design a location-label based (LLB) algorithm for protecting location privacy of users while minimizing the response time for LBS requests. We also evaluate the performance and validate the correctness of the proposed algorithm through extensive simulations. (C) 2016 Elsevier B.V. All rights reserved.