Hide Yourself: Multi-Dimensional Range Queries for Responses-Hiding Over Outsourced Data

Haoyang Wang, Kai Fan, Chong Yu, Kuan Zhang, Fenghua Li, Haojin Zhu

Abstract: Multi-dimensional range query (MRQ) over outsourced data has been extensively applied in various domains. However, security and efficiency are still two aspects that cannot be easily balanced in private MRQs, as improving security inevitably incurs high computation, storage, and communication costs. Several schemes perform encrypted data retrieval in the trusted execution environment (TEE), which balances security and performance. Unfortunately, they focused on keywords or single-dimensional range queries, failing to address private MRQs. With the TEE (i.e., Intel SGX), we propose a response-hiding MRQ scheme over encrypted data (SGX-MRQ) in this paper. We first design an index structure called SDic, which can achieve efficient range queries while hiding the responses to each query from the server. Moreover, based on the security properties of SGX, we construct the encrypted polynomials of each dimension on the enclave and implement the intersection computation of multi-attribute queries by the server, which greatly improves the system efficiency. We present the formal definition of SGX-MRQ and perform a rigorous proof. We implement a prototype of SGX-MRQ and conduct extensive experiments on real datasets. The evaluation results validate the feasibility of our scheme in practical applications.

External IDs:dblp:journals/tifs/WangFYZLZ25a