A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Peekaboo, I See Your Queries: Passive Attacks Against DSSE Via Intermittent Observations
Tekijät: Nie, Hao; Wang, Wei; Xu, Peng; Chen, Wei; Yang, Laurence T.; Conti, Mauro; Liang, Kaitai
Toimittaja: Huang, Chun-Ying; Chen, Jyh-Cheng; Shieh, Shiuh-Pyng; Lie, David; Cortier, Véronique
Konferenssin vakiintunut nimi: ACM SIGSAC Conference on Computer and Communications Security
Julkaisuvuosi: 2025
Lehti: ACM Conference on Computer and Communications Security
Kokoomateoksen nimi: CCS '25 : Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security
Aloitussivu: 2429
Lopetussivu: 2443
ISBN: 979-8-4007-1525-9
ISSN: 1543-7221
DOI: https://doi.org/10.1145/3719027.3765075
Julkaisun avoimuus kirjaamishetkellä: Ei avoimesti saatavilla
Julkaisukanavan avoimuus : Osittain avoin julkaisukanava
Verkko-osoite: https://doi.org/10.1145/3719027.3765075
Dynamic Searchable Symmetric Encryption (DSSE) allows secure searches over a dynamic encrypted database but suffers from inherent information leakage. Existing passive attacks against DSSE rely on persistent leakage monitoring to infer leakage patterns, whereas this work targets intermittent observation - a more practical threat model. We propose Peekaboo - a new universal attack framework - and the core design relies on inferring the search pattern and further combining it with auxiliary knowledge and other leakage. We instantiate Peekaboo over the SOTA attacks, Sap (USENIX' 21) and Jigsaw (USENIX' 24), to derive their ''+'' variants (Sap+ and Jigsaw+). Extensive experiments demonstrate that our design achieves >0.9 adjusted rand index for search pattern recovery and ∼90% query accuracy vs. FMA's ∼30% (CCS' 23). Peekaboo's accuracy scales with observation rounds and the number of observed queries but also it resists SOTA countermeasures, with >40% accuracy against file size padding and >80% against obfuscation.
Julkaisussa olevat rahoitustiedot:
This work was supported by the National Key Research and Development Program of China under Grant No. 2022YFB4501500 and the National Natural Science Foundation of China under Grant No. 62372201 and No. 62272186.
