Improved Reconstruction Attacks on Encrypted Data Using Range Query Leakage
Marie-Sarah Lacharité, Brice Minaud, Kenneth G. Paterson
Abstract
We analyse the security of database encryption schemes supporting range queries against persistent adversaries. The bulk of our work applies to a generic setting, where the adversary's view is limited to the set of records matched by each query (known as access pattern leakage). We also consider a more specific setting where rank information is also leaked, which is inherent inherent to multiple recent encryption schemes supporting range queries. We provide three attacks. First, we consider full reconstruction, which aims to recover the value of every record, fully negating encryption. We show that for dense datasets, full reconstruction is possible within an expected number of queries N log N + O(N), where N is the number of distinct plaintext values. This directly improves on a quadratic bound in the same setting by Kellaris et al. (CCS 2016). Second, we present an approximate reconstruction attack recovering all plaintext values in a dense dataset within a constant ratio of error, requiring the access pattern leakage of only O(N) queries. Third, we devise an attack in the common setting where the adversary has access to an auxiliary distribution for the target dataset. This third attack proves highly effective on age data from real-world medical data sets. In our experiments, observing only 25 queries was sufficient to reconstruct a majority of records to within 5 years. In combination, our attacks show that current approaches to enabling range queries offer little security when the threat model goes beyond snapshot attacks to include a persistent server-side adversary.
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Install the CLIlune papers fulltext cf5d8870-59e4-4d81-8f35-6bb9203fdd25Cited by top-tier papers49
- Pump up the Volume: Practical Database Reconstruction from Volume Leakage on Range QueriesPaul Grubbs, Marie-Sarah Lacharité, Brice Minaud, Kenneth G. PatersonCCS 2018 · 172 citations
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- Learning to Reconstruct: Statistical Learning Theory and Encrypted Database AttacksPaul Grubbs, Marie-Sarah Lacharité, Brice Minaud, Kenneth G. PatersonS&P 2019 · 146 citations
- The State of the Uniform: Attacks on Encrypted Databases Beyond the Uniform Query DistributionEvgenios M. Kornaropoulos, Charalampos Papamanthou, Roberto TamassiaS&P 2020 · 104 citations
- Data Recovery on Encrypted Databases with k-Nearest Neighbor Query LeakageEvgenios M. Kornaropoulos, Charalampos Papamanthou, Roberto TamassiaS&P 2019 · 92 citations
Builds on9
- All Your Queries Are Belong to Us: The Power of File-Injection Attacks on Searchable EncryptionYupeng Zhang, Jonathan Katz, Charalampos PapamanthouUSENIX Security 2016 · 512 citations
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- Leakage-Abuse Attacks against Order-Revealing EncryptionPaul Grubbs, Kevin Sekniqi, Vincent Bindschaedler, Muhammad Naveed et al.S&P 2017 · 204 citations
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- The Shadow Nemesis: Inference Attacks on Efficiently Deployable, Efficiently Searchable EncryptionDavid Pouliot, Charles V. WrightCCS 2016 · 132 citations
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