Security and Privacy in Federated Process Mining

In recent years, Process Mining (PM) has emerged as a transformative technology, enabling organisations to discover, analyse, and optimise their processes using event logs. Despite its widespread adoption, organisations, particularly those with complex structures and multiple subunits, often face significant challenges related to the sharing of raw event logs due to confidentiality concerns. To address this, Federated Process Mining (FPM) has been proposed as an innovative approach that facilitates collaborative process mining while preserving data privacy.However, for FPM to transition effectively from concept to real-world application, a thorough understanding of its security and privacy dimensions is crucial. This research delves into the critical aspects of securing and safeguarding privacy in FPM, identifying potential vulnerabilities, and proposing strategies to mitigate risks. By addressing these challenges, the study aims to enhance the reliability and trustworthiness of FPM as a robust solution for privacy-preserving process optimisation.

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Publication Details

Published
2024-12-11
DOI
https://doi.org/10.1109/iclt63507.2024.11038637
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
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Security and Privacy in Federated Process Mining

Eman Shaikh, Zeeshan Tariq, Sally McClean, Nazeeruddin Mohammad
Privacy-Preserving Technologies in Data
article

Security and Privacy in Federated Process Mining

Eman Shaikh, Zeeshan Tariq, Sally McClean, Nazeeruddin Mohammad
article en

Abstract

In recent years, Process Mining (PM) has emerged as a transformative technology, enabling organisations to discover, analyse, and optimise their processes using event logs. Despite its widespread adoption, organisations, particularly those with complex structures and multiple subunits, often face significant challenges related to the sharing of raw event logs due to confidentiality concerns. To address this, Federated Process Mining (FPM) has been proposed as an innovative approach that facilitates collaborative process mining while preserving data privacy.However, for FPM to transition effectively from concept to real-world application, a thorough understanding of its security and privacy dimensions is crucial. This research delves into the critical aspects of securing and safeguarding privacy in FPM, identifying potential vulnerabilities, and proposing strategies to mitigate risks. By addressing these challenges, the study aims to enhance the reliability and trustworthiness of FPM as a robust solution for privacy-preserving process optimisation.

Prince Mohammad bin Fahd University (SA), University of Ulster (GB)
Openalex Percentile: Top 72%
Privacy-Preserving Technologies in Data
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