Paper 2020/1501
PANCAKE: Frequency Smoothing for Encrypted Data Stores
Paul Grubbs, Anurag Khandelwal, Marie-Sarah Lacharité, Lloyd Brown, Lucy Li, Rachit Agarwal, and Thomas Ristenpart
Abstract
We present PANCAKE, the first system to protect key-value stores from access pattern leakage attacks with small constant factor bandwidth overhead. PANCAKE uses a new approach, that we call frequency smoothing, to transform plaintext accesses into uniformly distributed encrypted accesses to an encrypted data store. We show that frequency smoothing prevents access pattern leakage attacks by passive persistent adversaries in a new formal security model. We integrate PANCAKE into three key-value stores used in production clusters, and demonstrate its practicality: on standard benchmarks, PANCAKE achieves 229× better throughput than non-recursive Path ORAM - within 3–6× of insecure baselines for these key-value stores.
Metadata
- Available format(s)
-
PDF
- Category
- Cryptographic protocols
- Publication info
- Published elsewhere. Major revision. Usenix Security 2020
- Keywords
- encrypted search
- Contact author(s)
-
paulgrubbs12 @ gmail com
anurag khandelwal @ yale edu - History
- 2020-12-02: received
- Short URL
- https://ia.cr/2020/1501
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2020/1501,
author = {Paul Grubbs and Anurag Khandelwal and Marie-Sarah Lacharité and Lloyd Brown and Lucy Li and Rachit Agarwal and Thomas Ristenpart},
title = {{PANCAKE}: Frequency Smoothing for Encrypted Data Stores},
howpublished = {Cryptology {ePrint} Archive, Paper 2020/1501},
year = {2020},
url = {https://eprint.iacr.org/2020/1501}
}
