Author, as appears in the article.: Batista, Edgar; Solanas, Agusti
Department: Enginyeria Informàtica i Matemàtiques
URV's Author/s: Batista De Frutos, Edgar / Solanas Gómez, Agustín
Keywords: Uniformization strategies Process mining Privacy-preserving process mining Privacy Distribution-based attacks
Abstract: Process Mining is a set of techniques that aim at discovering, monitoring and improving real processes by using logs of events created and stored by corporate information systems. The growing use of information and communication technologies and the imminent wide deployment of the Internet of Things enable the massive collection of events, which are going to be studied so as to improve all kinds of systems efficiency. Despite its enormous benefits, analyzing event logs might endanger individuals privacy, especially when those logs contain personal and confidential information, such as healthcare data. This article contributes to an emerging research direction within the process mining field, known as Privacy-Preserving Process Mining (PPPM), which embraces the privacy-by-design principle when conducting process mining analyses. We show that current solutions based on pseudonyms and encryption are vulnerable to attacks based on the analysis of the distribution of events combined with well-known location-oriented attacks such as the restricted space identification and the object identification attacks. With the aim to counteract these attacks, we present u-PPPM, a novel privacy-preserving process mining technique based on the uniformization of events distributions. This approach protects the privacy of the individuals appearing in event logs while minimizing the information loss during process discovery analyses. Experimental results, conducted using six real-life event logs, demonstrate the feasibility of our approach in real settings.
Thematic Areas: Telecommunications Software Engenharias iv Computer science, information systems Computer networks and communications Ciência da computação
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: edgar.batista@urv.cat edgar.batista@urv.cat agusti.solanas@urv.cat
Author identifier: 0000-0002-4881-6215
Record's date: 2024-10-26
Papper version: info:eu-repo/semantics/acceptedVersion
Link to the original source: https://link.springer.com/article/10.1007/s12083-020-01059-1
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Peer-To-Peer Networking And Applications. 14 (3): 1500-1519
APA: Batista, Edgar; Solanas, Agusti (2021). A uniformization-based approach to preserve individuals' privacy during process mining analyses. Peer-To-Peer Networking And Applications, 14(3), 1500-1519. DOI: 10.1007/s12083-020-01059-1
Article's DOI: 10.1007/s12083-020-01059-1
Entity: Universitat Rovira i Virgili
Journal publication year: 2021
Publication Type: Journal Publications