Query Recovery from Easy to Hard: Jigsaw Attack against SSE

• URL: http://arxiv.org/abs/2403.01155v1
• Date: Sat, 2 Mar 2024 09:57:05 GMT
• Title: Query Recovery from Easy to Hard: Jigsaw Attack against SSE
• Authors: Hao Nie, Wei Wang, Peng Xu, Xianglong Zhang, Laurence T. Yang, Kaitai Liang,
• Abstract summary: symmetric encryption schemes often unintentionally disclose certain sensitive information, such as access, volume, and search patterns. We find that the effectiveness of query recovery attacks depends on the volume/frequency distribution of keywords. We propose a Jigsaw attack that begins by accurately identifying and recovering those distinctive queries.
• Score: 22.046278061025323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Searchable symmetric encryption schemes often unintentionally disclose certain sensitive information, such as access, volume, and search patterns. Attackers can exploit such leakages and other available knowledge related to the user's database to recover queries. We find that the effectiveness of query recovery attacks depends on the volume/frequency distribution of keywords. Queries containing keywords with high volumes/frequencies are more susceptible to recovery, even when countermeasures are implemented. Attackers can also effectively leverage these ``special'' queries to recover all others. By exploiting the above finding, we propose a Jigsaw attack that begins by accurately identifying and recovering those distinctive queries. Leveraging the volume, frequency, and co-occurrence information, our attack achieves $90\%$ accuracy in three tested datasets, which is comparable to previous attacks (Oya et al., USENIX' 22 and Damie et al., USENIX' 21). With the same runtime, our attack demonstrates an advantage over the attack proposed by Oya et al (approximately $15\%$ more accuracy when the keyword universe size is 15k). Furthermore, our proposed attack outperforms existing attacks against widely studied countermeasures, achieving roughly $60\%$ and $85\%$ accuracy against the padding and the obfuscation, respectively. In this context, with a large keyword universe ($\geq$3k), it surpasses current state-of-the-art attacks by more than $20\%$.

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