![]() ![]() We have studied the efficiency of our algorithm under different conditions using realistic workloads. The proposed location privacy frame work is implemented by an efficient TTP server. In our model, k-anonymization and pseudo-anonymization methods have been used hand in hand. We have developed an efficient LBS privacy protection algorithm. 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. A privacy-aware management of location information, which provides location privacy for clients against vulnerabilities or abuse, is very much needed. In LBS, the safety and security of data is one of the most important things to be taken care. Location based services (LBS) are one of the most commonly used services in Augmented Reality(AR).
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