Articles producció científica> Enginyeria Informàtica i Matemàtiques

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

  • Identification data

    Identifier: imarina:9150983
    Authors:
    Batista, EdgarSolanas, Agusti
    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.
  • Others:

    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
  • Keywords:

    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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