A privacy-preserving framework for compressed pattern matching over encrypted data using lightweight XOR-based encryption and bit-parallel processing

Abstract

With the tremendous growth of cloud computing, cyber threat intelligence systems, and compressed big-data storage, efficient and secure compressed pattern-matching techniques are in high demand. Traditional compressed pattern-matching methods mainly focus on computational efficiency and do not consider privacy preservation or pattern search over encrypted patterns. We propose a novel privacy-preserving compressed pattern-matching framework, the secure bit-parallel compressor (Secure BIT_COMP), which integrates word-based tagged code (WBTC), a bit-parallel Shift-Or algorithm, encrypted compressed search, and encrypted bit-level operations using a lightweight exclusive-OR (XOR)-based prototype encryption mechanism. The proposed methodology enables direct search without decompressing the text or revealing plaintext in encrypted and compressed text streams. Secure BIT_COMP uses bit-parallel matching and encrypted state transitions to search efficiently without compromising confidentiality. Furthermore, a secure verification invariant based on encrypted boundary validation helps prevent false positives. According to the theory, the proposed encrypted-compressed search framework achieves machine-word-level complexity for privacy-preserving search. Experiments on textual datasets show that the proposed method is more accurate than existing WBTC and tagged Huffman approaches and reduces memory usage while supporting secure matching. The proposed framework extends compressed pattern matching to secure cloud computing, encrypted genomic analysis, digital forensics, and privacy-preserving cybersecurity applications.