Articles producció científica> Enginyeria Informàtica i Matemàtiques

Privacy-preserving process mining: A microaggregation-based approach

  • Identification data

    Identifier: imarina:9266895
    Authors:
    Batista, EdgarMartinez-Balleste, AntoniSolanas, Agusti
    Abstract:
    The proper exploitation of vast amounts of event data by means of process mining techniques enables the discovery, monitoring and improvement of business processes, allowing organizations to develop more efficient business intelligence systems. However, event data often contain personal and/or confidential information that, unless properly managed, may jeopardize people's privacy while conducting process mining analysis. Despite its relevance, privacy aspects have barely been considered within process mining, and the field of privacy-preserving process mining is still in an embryonic stage. With the aim to protect people's privacy, this article presents a novel privacy-preserving process mining method based on microaggregation techniques, called k-PPPM, that increases privacy in process mining through k-anonymity. Contrary to current solutions, mostly based on pseudonyms and encryption, this method averts the re-identification of targeted individuals from attacks based on the analysis of process models in combination with location-oriented attacks, such as Restricted Space Identification and Object Identification attacks. The proposed method provides adjustable parameters to tune different anonymization aspects. Six real-life event logs have been employed to evaluate the method in terms of process models quality and information loss.
  • Others:

    Author, as appears in the article.: Batista, Edgar; Martinez-Balleste, Antoni; Solanas, Agusti
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Batista De Frutos, Edgar / Martínez Ballesté, Antoni / Solanas Gómez, Agustín
    Keywords: Process mining Privacy-preserving process mining Privacy preservation Microaggregation K-anonymity Confidentiality Anonymization process mining privacy preservation microaggregation k-anonymity health confidentiality anonymization
    Abstract: The proper exploitation of vast amounts of event data by means of process mining techniques enables the discovery, monitoring and improvement of business processes, allowing organizations to develop more efficient business intelligence systems. However, event data often contain personal and/or confidential information that, unless properly managed, may jeopardize people's privacy while conducting process mining analysis. Despite its relevance, privacy aspects have barely been considered within process mining, and the field of privacy-preserving process mining is still in an embryonic stage. With the aim to protect people's privacy, this article presents a novel privacy-preserving process mining method based on microaggregation techniques, called k-PPPM, that increases privacy in process mining through k-anonymity. Contrary to current solutions, mostly based on pseudonyms and encryption, this method averts the re-identification of targeted individuals from attacks based on the analysis of process models in combination with location-oriented attacks, such as Restricted Space Identification and Object Identification attacks. The proposed method provides adjustable parameters to tune different anonymization aspects. Six real-life event logs have been employed to evaluate the method in terms of process models quality and information loss.
    Thematic Areas: Software Safety, risk, reliability and quality Computer science, information systems Computer networks and communications
    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 antoni.martinez@urv.cat
    Author identifier: 0000-0002-4881-6215 0000-0002-1787-7410
    Record's date: 2024-10-26
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/pii/S2214212622001041
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Journal Of Information Security And Applications. 68 103235-
    APA: Batista, Edgar; Martinez-Balleste, Antoni; Solanas, Agusti (2022). Privacy-preserving process mining: A microaggregation-based approach. Journal Of Information Security And Applications, 68(), 103235-. DOI: 10.1016/j.jisa.2022.103235
    Article's DOI: 10.1016/j.jisa.2022.103235
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Journal Publications
  • Keywords:

    Computer Networks and Communications,Computer Science, Information Systems,Safety, Risk, Reliability and Quality,Software
    Process mining
    Privacy-preserving process mining
    Privacy preservation
    Microaggregation
    K-anonymity
    Confidentiality
    Anonymization
    process mining
    privacy preservation
    microaggregation
    k-anonymity
    health
    confidentiality
    anonymization
    Software
    Safety, risk, reliability and quality
    Computer science, information systems
    Computer networks and communications
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