Efficient Volume-Hiding Encrypted Conjunctive Search With Leakage Suppression for Cloud-Assisted IoT

In resource-constrained environments such as IoT sensors and mobile devices, there is a strong demand for efficient conjunctive keyword search over privacy-sensitive data. However, existing schemes struggle to simultaneously suppress sterm equality leakage, the cross-query intersection pattern (IP), and the volume pattern without incurring prohibitive overhead. In this paper, we present XORCMM, a practical volume-hiding encrypted conjunctive multi-map (EMM) designed for robust leakage suppression. First, we shift the index construction from single keywords to global-ordering co-occurrence pairs, which ensures that search tokens are no longer tied to static keyword identities, thereby suppressing sterm equality leakage. Second, we integrate an incremental multiset hash aggregation mechanism directly into a fully padded Xor filter. This allows the server to aggregate multiple conjunctive results into a single, fixed length response, concealing both IP and volume patterns while eliminating the data redundancy of prior schemes. Third, we employ a prefix-constrained PRF to compactly encode keyword pairs, generating succinct query tokens whose size is independent of keyword volumes. Formal security analysis proves that XORCMM is adaptively secure with sterm equality, IP, and volume leakages hidden. Experimental results demonstrate that XORCMM achieves up to a 2.99× speedup in client setup, a 3.3× speedup in server query time, and reductions of 47% in response size and 84.61% in search token size, providing a stronger security guarantee with significantly higher efficiency.