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Marie-Sarah Lacharité
Marie-Sarah Lacharité
Verified email at rhul.ac.uk
Title
Cited by
Cited by
Year
Improved reconstruction attacks on encrypted data using range query leakage
MS Lacharité, B Minaud, KG Paterson
2018 IEEE Symposium on Security and Privacy (SP), 297-314, 2018
2122018
Pump up the volume: Practical database reconstruction from volume leakage on range queries
P Grubbs, MS Lacharité, B Minaud, KG Paterson
Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications …, 2018
1662018
Learning to reconstruct: Statistical learning theory and encrypted database attacks
P Grubbs, MS Lacharité, B Minaud, KG Paterson
2019 IEEE Symposium on Security and Privacy (SP), 1067-1083, 2019
1402019
Pancake: Frequency smoothing for encrypted data stores
P Grubbs, A Khandelwal, MS Lacharité, L Brown, L Li, R Agarwal, ...
29th USENIX Security Symposium (USENIX Security 20), 2451-2468, 2020
692020
Frequency-smoothing encryption: preventing snapshot attacks on deterministically encrypted data
MS Lacharité, KG Paterson
Cryptology ePrint Archive, 2017
242017
A note on the optimality of frequency analysis vs. -optimization
MS Lacharité, KG Paterson
Cryptology ePrint Archive, 2015
172015
Security of BLS and BGLS signatures in a multi-user setting
MS Lacharité
Cryptography and Communications 10 (1), 41-58, 2018
162018
Breaking encrypted databases: Generic attacks on range queries
MS Lacharité
Technical report, Black Hat USA, 2019. http://i. blackhat. com/USA-19 …, 2019
12019
Building and breaking encrypted search schemes for ordered data
MS Lacharité
Royal Holloway, University of London, 2020
2020
Pump up the Volume
P Grubbs, MS Lacharite, B Minaud, KG Paterson
Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications …, 2018
2018
Revisiting the security model for aggregate signature schemes
MS Lacharité
University of Waterloo, 2014
2014
Learning to Reconstruct
P Grubbs, MS Lacharité, B Minaud, K Paterson
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Articles 1–12