Strengthening Order Preserving Encryption with Differential Privacy
Amrita Roy Chowdhury, Bolin Ding, Somesh Jha, Weiran Liu, Jingren Zhou
摘要
Ciphertexts of an order-preserving encryption (OPE) scheme preserve the order of their corresponding plaintexts. However, OPEs are vulnerable to inference attacks that exploit this preserved order. Differential privacy (DP) has become the de-facto standard for data privacy. One of the most attractive properties of DP is that any post-processing computation, such as inference attacks, performed on the noisy output of a DP algorithm does not degrade its privacy guarantee. In this work, we propose a novel differentially private order preserving encryption scheme, OP ε. Under OP ε, the leakage of order from the ciphertexts is differentially private. Consequently, in the least, OP ε ensures a formal guarantee (a relaxed DP guarantee) even in the face of inference attacks. To the best of our knowledge, this is the first work to combine DP with a OPE. OP ε is based on a novel differentially private order preserving encoding scheme, OPεc, that can be of independent interest in the local DP setting. We demonstrate OP ε's utility in answering range queries via empirical evaluation on four real-world datasets. For instance, OP ε misses only around 4 in every 10K correct records on average for a dataset of size 732K with an attribute of domain size 18K and ε= 1.
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引用它的顶会 Paper5
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- Frequency-revealing attacks against Frequency-hiding Order-preserving EncryptionXinle Cao, Jian Liu, Yongsheng Shen, Xiaohua Ye 等VLDB 2023 · 被引用 9 次
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- Metric Differential Privacy at the User-Level via the Earth-Mover's DistanceJacob Imola, Amrita Roy Chowdhury, Kamalika ChaudhuriCCS 2024
- Differentially Private Access in Encrypted Search: Achieving Privacy at a Small Cost?Daniel Pöllmann, Tianxin TangCCS 2025
它引用的顶会 Paper22
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- The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure AggregationPeter Kairouz, Ziyu Liu, Thomas SteinkeICML 2021 · 被引用 291 次
- Leakage-Abuse Attacks against Order-Revealing EncryptionPaul Grubbs, Kevin Sekniqi, Vincent Bindschaedler, Muhammad Naveed 等S&P 2017 · 被引用 204 次
- Improved Reconstruction Attacks on Encrypted Data Using Range Query LeakageMarie-Sarah Lacharité, Brice Minaud, Kenneth G. PatersonS&P 2018 · 被引用 183 次
- Pump up the Volume: Practical Database Reconstruction from Volume Leakage on Range QueriesPaul Grubbs, Marie-Sarah Lacharité, Brice Minaud, Kenneth G. PatersonCCS 2018 · 被引用 172 次
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