Efficient Privacy-Preserving Range Query With Leakage Suppressed for Encrypted Data in Cloud-Based Internet of Things

Data owners are increasingly drawn to outsource their data due to the massive processing capabilities and scalable storage offered by cloud-based Internet of Things (IoT) solutions. To protect user privacy, the acquired data may be encrypted; however, this often presents challenges for efficiently searching the data. Although encrypted range queries are among the most commonly used techniques and have been extensively studied, several issues persist within current systems. Specifically, these systems may inadvertently disclose the order relationship between the encrypted index and the upper and lower bounds of a range query, rendering them vulnerable to inference attacks that enable an attacker to uncover the user’s attribute values. To address these challenges, we propose an effective privacy-preserving range query with leakage suppressed approach. The proposed method transforms range comparisons into set intersections, thereby successfully concealing the relationship between the encrypted index and the upper and lower bounds. Consequently, to perform range queries safely and efficiently, we construct an encrypted bloom filter. This ensures that the proposed plan can effectively thwart inference attacks. Furthermore, we incorporate a point-value function polynomial technique into the proposed protocol to facilitate straightforward verification of the query results. A comprehensive security analysis demonstrates the effectiveness of our approach in achieving the stated design goals. Performance evaluations illustrate the practical applicability of the proposed scheme by demonstrating its scalability and feasibility.