# Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration

arXiv: https://arxiv.org/abs/2211.15732 · HTML: https://mlsanity.com/paper/2211.15732/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2022.

## Built on by (most cited first, 1 of 1)

- [Answering Private Linear Queries Adaptively using the Common Mechanism](https://mlsanity.com/paper/2212.00135/) (2022)

## Abstract (arXiv)

Differential privacy (DP) allows data analysts to query databases that contain users' sensitive information while providing a quantifiable privacy guarantee to users. Recent interactive DP systems such as APEx provide accuracy guarantees over the query responses, but fail to support a large number of queries with a limited total privacy budget, as they process incoming queries independently from past queries. We present an interactive, accuracy-aware DP query engine, CacheDP, which utilizes a differentially private cache of past responses, to answer the current workload at a lower privacy budget, while meeting strict accuracy guarantees. We integrate complex DP mechanisms with our structured cache, through novel cache-aware DP cost optimization. Our thorough evaluation illustrates that CacheDP can accurately answer various workload sequences, while lowering the privacy loss as compared to related work.