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EPAR: An Efficient and Privacy-Aware Augmented Reality Framework for Indoor Location-Based Services

Augmented reality (AR) defines a new information-delivery paradigm by overlaying computer-generated information on the perception of the real world. AR-integrated robot has become an appealing concept in terms of enhanced human-robot interaction. Despite intensive research on AR, existing indoor location-based AR systems are vulnerable to attacks and can hardly meet the security and privacy requirements in practice. The problem of designing a secure AR framework to ensure the efficiency and privacy of location-based AR has not been sufficiently studied. In this paper, we holistically study this problem and propose EPAR, an efficient and privacy-aware AR framework for indoor location-based services. EPAR distinguishes itself from the existing work by being the first to address the issues of AR delivery in terms of system scalability, accuracy, privacy, and efficiency. First, an effective indoor location cloaking scheme is presented to safeguard user's privacy while improving system scalability and accuracy. Then, a novel privacy-aware localization scheme is proposed to hierarchically localize the user with privacy concerns. Finally, for the AR content delivery, a new authenticated data structure is tailored to save the data transmission cost and improve system efficiency. We implement EPAR and conduct extensive experiments in real-world scenarios. Evaluation results demonstrate the effectiveness of our EPAR system.