VPRQ: Verifiable and privacy-preserving range query over encrypted cloud data

Abstract

Cloud has scalable storage and massive computing power, attracting data owners to outsource their data. However, owing to privacy concerns, data is typically encrypted prior to outsourcing, which inevitably presents challenges for querying the data effectively. Although encrypted range query is one of the most prevalent types and has been widely studied, existing schemes still have some problems. They inadvertently disclose the order relationship between the upper/lower bound of a range query and the encrypted index, leading to vulnerability inference attack. Moreover, the cloud server cannot be fully trusted, which may return incorrect and incomplete query results. To deal with these issues, we present a novel verifiable and privacy-preserving range query scheme (VPRQ). The VPRQ scheme utilizes 0/1 technique to transform range comparisons into set intersections. By doing so, it effectively hides the relationship between the upper/lower bound and the encrypted index. On this basis, we design an encrypted garbled bloom filter to securely and effectively achieve range query. This ensures that VPRQ scheme effectively resists inference attack. Additionally, point-value polynomial function technology is integrated into the VPRQ protocol to provide lightweight verification for the query results. Comprehensive security analysis and proof demonstrate its effectiveness in achieving the intended design objectives. Performance evaluations illustrate the feasibility and scalability of the proposed scheme, highlighting its practical applicability.