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TPSA 2021 : 2nd Workshop on Trust, Privacy and Security Aspects in Process Analytics

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Link: https://tpsa-workshop.github.io/2021/
 
When Oct 31, 2021 - Nov 4, 2021
Where Eindhoven
Abstract Registration Due Aug 19, 2021
Submission Deadline Aug 26, 2021
Notification Due Sep 16, 2021
Final Version Due Sep 30, 2021
Categories    responsible data science   business process management   privacy   security
 

Call For Papers

Process Mining has been successfully applied in analysing and improving processes based on event logs in all kinds of environments. However, aspects of trust, privacy, and security have been largely neglected when considering the technical design as well as the organizational application of Process Mining. These aspects can be seen from two perspectives:

(A) Responsible application of Process Mining and the trust that the methods and their results cannot or are not misused. Criteria for responsible data science have been defined, e.g., the FACT (Fairness, Accuracy, Confidentiality, Transparency) criteria. How should these criteria be embedded in the design and the application of Process Mining methods.

(B) Investigating privacy and security aspects of information systems by using Process Mining methods. How can Process Mining be used to find privacy violations or security issues that undermine the trust in systems.

For us, privacy relates to the concern that event logs may contain personal data of both customers and employees and the challenge of protecting the information about individuals while still being useful for process mining (e.g., differential privacy, k-anonymity, homomorphic encryption, secure multi-party computing). However, Process Mining could also be used to investigate whether privacy regulations are being followed and pinpoint compliance violations. Often, security aspects (e.g., encryption) are closely connected when processing personal data cannot be avoided. On the other hand, the workshop is about the concept of trust, which is required both from the perspective of trust in organizational and technological measures that event logs are not misused (e.g., for worker surveillance) as well as from the perspective of trust that the results of a process mining analysis faithfully reflect reality (e.g., data quality, traceability, auditability).

The main objective of the TPSA workshop is to give a forum for the trust, privacy, and security aspects and the responsible application of process mining including other concerns such as fairness, transparency, and accuracy. We invite researchers and industry to share their research, ideas, experience reports, and challenges in this area. The best papers submitted to the workshop will be invited to submit a revised version to a special issue of the EMISA Journal.

The topics of interest for this workshop, but not limited to, are provided below.
- Privacy-preserving methods for Process Analytics (Process Mining, Data Mining, Machine Learning)
- Privacy-preserving methods for Business Process Management
- Privacy and Trust for Blockchains in Business Process Management
- Privacy Engineering for Event Logs
- Privacy and Trust in Organizational Data Collection
- Analysis of Privacy Compliance with Process Analytics
- GDPR and Process Mining
- Data Quality, and Traceability in Process Analytics
- Methods and Techniques for Privacy and Trust Management in Process Mining
- Trust in Process Analytics
- Fairness in Process Analytics
- Responsible Process Analytics
- Trust in Explainable Process Analytics

Organizing Committee

- Felix Mannhardt, TU Eindhoven
- Agnes Koschmider, Kiel University
- Nathalie Baracaldo, IBM Almaden Research Center

Program committee (tentative)

- Luciano Garcia BaƱuelos, Tecnologico de Monterrey, Mexico
- Olivia Choudhury, Amazon Inc.
- Stephan Fahrenkrog-Petersen, Humboldt University of Berlin
- Marwan Hassani, TU Eindhoven
- Judith Michael, RWTH Aachen
- Florian Tschorsch, Technical University Berlin
- Melanie Volkamer, Karlsruhe Institute of Technology
- Moe Wynn, Queensland University of Technology
- Sebastiaan van Zelst, RWTH Aachen / FIT

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