Differentially Private Tree-Based Redescription Mining

TL;DR

This paper introduces three novel tree-based algorithms for differentially private redescription mining, enabling privacy-preserving exploration of data connections in sensitive datasets like healthcare, with promising experimental results.

Contribution

It presents the first differentially private algorithms for redescription mining, combining privacy guarantees with effective data analysis in sensitive domains.

Findings

Algorithms produce trustworthy results despite privacy-induced noise.

Effective on small datasets where noise impact is usually higher.

Demonstrates practical applicability in health informatics.

Abstract

Differential privacy provides a strong form of privacy and allows preserving most of the original characteristics of the dataset. Utilizing these benefits requires one to design specific differentially private data analysis algorithms. In this work, we present three tree-based algorithms for mining redescriptions while preserving differential privacy. Redescription mining is an exploratory data analysis method for finding connections between two views over the same entities, such as phenotypes and genotypes of medical patients, for example. It has applications in many fields, including some, like health care informatics, where privacy-preserving access to data is desired. Our algorithms are the first differentially private redescription mining algorithms, and we show via experiments that, despite the inherent noise in differential privacy, it can return trustworthy results even in smaller datasets where noise typically has a stronger effect.