LAUSR

Range queries are widely used in several Internet-of-Things (IoT) applications as a general strategy to improve the efficiency of the system. However, the communication patterns generated by the IoT nodes could lead to the identification of the devices satisfying the query, as well as to the disclosure of the queried data. State-of-the-art solutions to address the cited security issues rely on dedicated edge/fog nodes, whose deployment could be too expensive or challenging, especially in unattended scenarios where the installation of ad hoc locations could be difficult and mains-supply is hardly available. In this article, we propose PPRQ, a resilient, scalable, and lightweight protocol that allows privacy-preserving range queries in IoT networks. PPRQ is a probabilistic scheme that can be easily adapted to MIN, MAX, and MAX/MIN range queries, while requiring only hashing and bitwise xor operations. We show that PPRQ is robust, as it can be configured to provide over 99.9% accuracy in the query results. We also prove its resiliency against passive and active adversaries for a number of interesting and realistic scenarios. Our results are rooted in sound probability theory and supported by an extensive simulation campaign, while comparisons against state-of-the-art solutions show the flexibility and adaptability of PPRQ, especially for remote and unattended scenarios. Finally, further research directions opened up by the proposed solution are also highlighted.