BRASP: Boolean Range Queries over Encrypted Spatial Data with Access and Search Pattern Privacy
2026-04-09 • Cryptography and Security
Cryptography and Security
AI summaryⓘ
The authors developed BRASP, a method that lets users search encrypted location-based data without revealing what they’re searching for or which results come up. It works with complex queries involving ranges and multiple keywords by using a technique based on Hilbert curves combined with special encrypted indexes. To hide search and access patterns, their system uses two cloud servers that work together but do not share information, mixing up data and redistributing IDs to keep things private. They also made sure BRASP can handle updates securely and tested it on real data, finding it to be efficient and protective of privacy.
Searchable EncryptionHilbert CurveBoolean Range QueriesEncrypted IndexesAccess Pattern LeakageDual-Server ModelForward SecurityQuery PrivacyIndex ShufflingConjunctive Keyword Matching
Authors
Jing Zhang, Ganxuan Yang, Yifei Yang, Siqi Wen, Zhengyang Qiu
Abstract
Searchable Encryption (SE) enables users to query outsourced encrypted data while preserving data confidentiality. However, most efficient schemes still leak the search pattern and access pattern, which may allow an honest-but-curious cloud server to infer query contents, user interests, or returned records from repeated searches and observed results. Existing pattern-hiding solutions mainly target keyword queries and do not naturally support Boolean range queries over encrypted spatial data. This paper presents BRASP, a searchable encryption scheme for Boolean range queries over encrypted spatial data. BRASP combines Hilbert-curve-based prefix encoding with encrypted prefix--ID and keyword--ID inverted indexes to support efficient spatial range filtering and conjunctive keyword matching. To hide the search pattern and access pattern under a dual-server setting, BRASP integrates index shuffling for encrypted keyword and prefix entries with ID-field redistribution across two non-colluding cloud servers. BRASP also supports dynamic updates and achieves forward security. We formalize the security of BRASP through confidentiality, shuffle indistinguishability, query unforgeability, and forward-security analyses, and we evaluate its performance experimentally on a real-world dataset. The results show that BRASP effectively protects query privacy while incurring relatively low computation and communication overhead. To facilitate reproducibility and further research, the source code of BRASP is publicly available at https://github.com/Egbert-Lannister/BRASP