# $\mathcal{E}\text{psolute}$: Efficiently Querying Databases While Providing Differential Privacy

## TL;DR

This paper introduces $ ext{Epsolute}$, a scalable, efficient system that enables privacy-preserving database queries using differential privacy and cryptographic techniques, suitable for real-world deployment.

## Contribution

The work presents a novel, practical construction combining Oblivious RAM and differential privacy for secure outsourced databases, with improvements for efficiency and scalability.

## Key findings

- Supports up to 10 million records with high efficiency
- Less than tenfold slowdown compared to standard RDBMS
- Proven privacy guarantees against untrusted servers

## Abstract

As organizations struggle with processing vast amounts of information, outsourcing sensitive data to third parties becomes a necessity. To protect the data, various cryptographic techniques are used in outsourced database systems to ensure data privacy, while allowing efficient querying. A rich collection of attacks on such systems has emerged. Even with strong cryptography, just communication volume or access pattern is enough for an adversary to succeed. In this work we present a model for differentially private outsourced database system and a concrete construction, $\mathcal{E}\text{psolute}$, that provably conceals the aforementioned leakages, while remaining efficient and scalable. In our solution, differential privacy is preserved at the record level even against an untrusted server that controls data and queries. $\mathcal{E}\text{psolute}$ combines Oblivious RAM and differentially private sanitizers to create a generic and efficient construction. We go further and present a set of improvements to bring the solution to efficiency and practicality necessary for real-world adoption. We describe the way to parallelize the operations, minimize the amount of noise, and reduce the number of network requests, while preserving the privacy guarantees. We have run an extensive set of experiments, dozens of servers processing up to 10 million records, and compiled a detailed result analysis proving the efficiency and scalability of our solution. While providing strong security and privacy guarantees we are less than an order of magnitude slower than range query execution of a non-secure plain-text optimized RDBMS like MySQL and PostgreSQL.