On the privacy protection of indoor location dataset using anonymization

Abstract

Indoor positioning is becoming more popular with increasing user demands on Location-based Services (LBS) and Social Networking Services (SNS). Location fingerprinting is widely deployed in indoor localization, and extensive daily use of different kinds of mobile devices in indoor environments results in generating a huge amount of location data of people. However, publishing this location based dataset by Location Service Provider (LSP) to any third party, such as researchers or commercial organizations, potentially threatens users’ privacy. Several studies have been done to anonymize the user location data, but protecting the users’ information against various attacks in an indoor environment is still a challenging issue. This paper proposes a novel framework for publishing privacy preserving location data employing five anonymization techniques: k-anonymity, ℓ-diversity, t-closeness, (α,k)-anonymity, and δ-presence. In the proposed framework, although the LSP cannot find the exact location of the users, it can provide them online location services at the same time and publish the anonymized privacy protected dataset afterward. The practical feasibility of applying the proposed framework is verified on both simulated and two real-world datasets. The results indicate that the published location dataset can protect the identity and location information of users while providing location information for third parties for further purposes.