Articles producció científica> Enginyeria Informàtica i Matemàtiques

A uniformization-based approach to preserve individuals' privacy during process mining analyses

  • Dades identificatives

    Identificador: imarina:9150983
    Autors:
    Batista, EdgarSolanas, Agusti
    Resum:
    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.
  • Altres:

    Autor segons l'article: Batista, Edgar; Solanas, Agusti
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Batista De Frutos, Edgar / Solanas Gómez, Agustín
    Paraules clau: Uniformization strategies Process mining Privacy-preserving process mining Privacy Distribution-based attacks
    Resum: 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.
    Àrees temàtiques: Telecommunications Software Engenharias iv Computer science, information systems Computer networks and communications Ciência da computação
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: edgar.batista@urv.cat edgar.batista@urv.cat agusti.solanas@urv.cat
    Identificador de l'autor: 0000-0002-4881-6215
    Data d'alta del registre: 2024-10-26
    Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
    Enllaç font original: https://link.springer.com/article/10.1007/s12083-020-01059-1
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Peer-To-Peer Networking And Applications. 14 (3): 1500-1519
    Referència de l'ítem segons les normes 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
    DOI de l'article: 10.1007/s12083-020-01059-1
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2021
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Computer Networks and Communications,Computer Science, Information Systems,Software,Telecommunications
    Uniformization strategies
    Process mining
    Privacy-preserving process mining
    Privacy
    Distribution-based attacks
    Telecommunications
    Software
    Engenharias iv
    Computer science, information systems
    Computer networks and communications
    Ciência da computação
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