Privacy-Preserving Lightweight Data Monitoring in Internet of Things Environments

Abstract

The fast development of Internet of Things (IoT) has shown that it becomes one of the most popular techniques. In the IoT paradigm, ubiquitous sensors and smart devices can be interconnected to collect various status data and share with others. When deployed in an environment status monitoring system, distributed sensors may be requested to periodically report real-time data. The large-scale data would make the system controller unable to process in time. In this case, a third-party server can be engaged to conduct most of monitoring work, where sensors direct report to the server to generate intermediate monitoring results for the system controller. However, the server may be curious about the contents of outsourced system standing queries, data vectors of sensors, and monitoring results. In addition, due to the limited computing resources of distributed sensors, existing cryptographic solutions are not applicable to such monitoring scenario. To address these issues, this paper proposes a lightweight server-aided data monitoring scheme (SIM). Thorough efficiency and privacy analysis confirm the practicality of the proposed SIM scheme. Moreover, this paper extends Lu et al.’s privacy-preserving cosine similarity computing protocol in the two-party setting in big data environment to support computing on any dimensional data, without incurring expensive calculations.