Leakage-abuse Attack Against Substring-SSE with Partially Known Dataset

TL;DR

This paper introduces the first leakage-abuse attack on substring-SSE with partially known datasets, demonstrating high success rates and exposing significant privacy vulnerabilities in current schemes.

Contribution

It develops a novel matrix-based correlation technique extending the LEAP framework to attack substring-SSE under partial knowledge, a previously unexplored scenario.

Findings

Achieves 98.32% recovery with 50% prior knowledge

Attains 74.42% recovery with only 10% prior knowledge

Demonstrates scalability across various dataset sizes

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.