Naslov
Differentially private tree-based redescription mining

Autori
Mihelčić, Matej ; Miettinen, Pauli

Vrsta, podvrsta
Radovi u časopisima, znanstveni

Izvornik
Data mining and knowledge discovery (2023)

Status rada
Online first

Ključne riječi
redescription mining ; differential privacy ; health care informatics

Sažetak
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 tree-based 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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti

POVEZANOST RADA