{"ID":2847023,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00930","arxiv_id":"2511.00930","title":"Leakage-abuse Attack Against Substring-SSE with Partially Known Dataset","abstract":"Substring-searchable symmetric encryption (substring-SSE) has become increasingly critical for privacy-preserving applications in cloud systems. However, existing schemes remain vulnerable to information leakage during search operations, particularly when adversaries possess partial knowledge of the target dataset. Although leakage-abuse attacks have been widely studied for traditional SSE, their applicability to substring-SSE under partially known data assumptions remains unexplored. In this paper, we present the first leakage-abuse attack on substring-SSE under partially-known dataset conditions. We develop a novel matrix-based correlation technique that extends and optimizes the LEAP framework for substring-SSE, enabling efficient recovery of plaintext data from encrypted suffix tree structures. Unlike existing approaches that rely on independent auxiliary datasets, our method directly exploits known data fragments to establish high-confidence mappings between ciphertext tokens and plaintext substrings through iterative matrix transformations. Comprehensive experiments on real-world datasets demonstrate the effectiveness of the attack, with recovery rates reaching 98.32% for substrings given 50% auxiliary knowledge. Even with only 10% prior knowledge, the attack achieves 74.42% substring recovery while maintaining strong scalability across datasets of varying sizes. The result reveals significant privacy risks in current substring-SSE designs and highlights the urgent need for leakage-resilient constructions.","short_abstract":"Substring-searchable symmetric encryption (substring-SSE) has become increasingly critical for privacy-preserving applications in cloud systems. However, existing schemes remain vulnerable to information leakage during search operations, particularly when adversaries possess partial knowledge of the target dataset. Alt...","url_abs":"https://arxiv.org/abs/2511.00930","url_pdf":"https://arxiv.org/pdf/2511.00930v1","authors":"[\"Xijie Ba\",\"Qin Liu\",\"Xiaohong Li\",\"Jianting Ning\"]","published":"2025-11-02T13:12:19Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[]","has_code":false}