# Strengthening Order Preserving Encryption with Differential Privacy

arXiv: https://arxiv.org/abs/2009.05679 · HTML: https://mlsanity.com/paper/2009.05679/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Stereo-LiDAR Fusion by Semi-Global Matching With Discrete Disparity-Matching Cost and Semidensification](https://mlsanity.com/paper/2504.05148/) (2025)
- [PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attacks](https://mlsanity.com/paper/2512.12840/) (2025)

## Abstract (arXiv)

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. At another end, differential privacy has become the de-facto standard for achieving data privacy. One of the most attractive properties of DP is that any post-processing (inferential) computation performed on the noisy output of a DP algorithm does not degrade its privacy guarantee. In this paper, we propose a novel differentially private order preserving encryption scheme, OPε. Under OPε, the leakage of order from the ciphertexts is differentially private. As a result, in the least, OPε ensures a formal guarantee (specifically, 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 property-preserving encryption scheme. We demonstrate OPε's practical utility in answering range queries via extensive empirical evaluation on four real-world datasets. For instance, OPε 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.