Protecting location privacy in Augmented Reality using k-anonymization and pseudo-id

Abstract

Location based services (LBS) are one of the most commonly used services in Augmented Reality(AR). In LBS, the safety and security of data is one of the most important things to be taken care. A privacy-aware management of location information, which provides location privacy for clients against vulnerabilities or abuse, is very much needed. This paper discusses how to protect the location privacy from various privacy threats, which occurred because of the unlimited usage of LBS, by a scalable architecture. We have developed an efficient LBS privacy protection algorithm. In our model, k-anonymization and pseudo-anonymization methods have been used hand in hand. The proposed location privacy frame work is implemented by an efficient TTP server. We have studied the efficiency of our algorithm under different conditions using realistic workloads. Our experiment shows that the k-anonymization and the pseudo-anonymization methods used together in our algorithm provide an efficient location privacy.