Privacy and Confidentiality in Process Mining: Threats and Research Challenges

Elkoumy, Gamal; Fahrenkrog-Petersen, Stephan A.; Sani, Mohammadreza Fani; Koschmider, Agnes; Mannhardt, Felix; Voigt, Saskia Nuñez von; Rafiei, Majid; Waldthausen, Leopold von

doi:10.1145/3468877

Cited by 29 publications

(13 citation statements)

References 47 publications

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“…Please note that we use protection here in the sense of anononymity and unlinkability requirements. Next to those, other requirements such as notice, transparency, and accountability are often imposed by regulations [45]. Note that most privacy-preserving techniques differ from the wide variety of best-effort pseudonymization, perturbation, and generalization Fig.…”

Section: Process Mining Perspectivementioning

confidence: 99%

“…Several attacks on confidential data in event logs are possible. We follow Elkoumy et al [45] and focus on a honest-but-curious attacker scenario. An adversary has access to data or results and tries to identify some sensitive information without trying to break into systems.…”

Section: Threats and Attacksmentioning

confidence: 99%

“…Given an event log and some sensitive information that is related to that log, a confidentiality attack uses some background knowledge, which may be derived from the log or from other available sources, to reveal some subset of sensitive information that is part of the log. We distinguish four general types of threats based on the goal of an attacker and the employed attack method following the categorization in [45].…”

Section: Threats and Attacksmentioning

confidence: 99%

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Responsible Process Mining

Mannhardt

2022

Lecture Notes in Business Information Processing

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The prospect of data misuse negatively affecting our life has lead to the concept of responsible data science. It advocates for responsibility to be built, by design, into data management, data analysis, and algorithmic decision making techniques such that it is made difficult or even impossible to intentionally or unintentionally cause harm. Process mining techniques are no exception to this and may be misused and lead to harm. Decisions based on process mining may lead to unfair decisions causing harm to people by amplifying the biases encoded in the data by disregarding infrequently observed or minority cases. Insights obtained may lead to inaccurate conclusions due to failing to considering the quality of the input event data. Confidential or personal information on process stakeholders may be leaked as the precise work behavior of an employee can be revealed. Process mining models are usually white-box but may still be difficult to interpret correctly without expert knowledge hampering the transparency of the analysis. This chapter structures the topic of responsible process mining based on the FACT criteria: Fairness, Accuracy, Confidentiality, and Transparency. For each criteria challenges specific to process mining are provided and the current state of the art is briefly summarized.

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Section: Process Mining Perspectivementioning

confidence: 99%

Section: Threats and Attacksmentioning

confidence: 99%

Section: Threats and Attacksmentioning

confidence: 99%

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Responsible Process Mining

Mannhardt

2022

Lecture Notes in Business Information Processing

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“…AI is too important and too promising to be governed in a hands-off fashion, waiting for problems to develop and then trying to fix them after the fact". 6 3 The Proposed EU Regulation of AI On April 20, 2021 the European Commission released the proposal for the regulation of artificial intelligence 7 , the ambition of which is to balance the socioeconomic benefits of AI and new risks or negative consequences for individuals or society. The proposed Artificial Intelligence Act (AIA) takes a risk-based approach to regulate AI by fostering an "ecosystem of trust that should give citizens the confidence to take up AI applications and give companies and public organisations the legal certainty to innovate using AI".…”

Section: The Emergence Of Trustworthy Ai Principlesmentioning

confidence: 99%

“…However, providing each aspect of RDS has its own challenges from contextualizing the aspect to implementing it in data science and AI systems. In [6], the authors describe the challenges regarding the confidentiality aspect for process mining which combines process and data science. In the following, we provide the challenges regarding the fairness aspect.…”

Section: Responsible Data Science and Trustworthy Aimentioning

confidence: 99%

Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities

Pery¹,

Rafiei²,

Simon³

et al. 2021

Preprint

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The premise of this paper is that compliance with Trustworthy AI governance best practices and regulatory frameworks is an inherently fragmented process spanning across diverse organizational units, external stakeholders, and systems of record, resulting in process uncertainties and in compliance gaps that may expose organizations to reputational and regulatory risks. Moreover, there are complexities associated with meeting the specific dimensions of Trustworthy AI best practices such as data governance, conformance testing, quality assurance of AI model behaviors, transparency, accountability, and confidentiality requirements. These processes involve multiple steps, hand-offs, re-works, and human-in-the-loop oversight. In this paper, we demonstrate that process mining can provide a useful framework for gaining fact-based visibility to AI compliance process execution, surfacing compliance bottlenecks, and providing for an automated approach to analyze, remediate and monitor uncertainty in AI regulatory compliance processes.

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Privacy-Preserving Continuous Event Data Publishing

Rafiei

Aalst

2021

Lecture Notes in Business Information Processing

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Process mining employs event data extracted from different types of information systems to discover and analyze actual processes. Event data often contain highly sensitive information about the people who carry out activities or the people for whom activities are performed. Therefore, privacy concerns in process mining are receiving increasing attention. To alleviate privacy-related risks, several privacy preservation techniques have been proposed. Differential privacy is one of these techniques which provides strong privacy guarantees. However, the proposed techniques presume that event data are released in only one shot, whereas business processes are continuously executed. Hence, event data are published repeatedly, resulting in additional risks. In this paper, we demonstrate that continuously released event data are not independent, and the correlation among different releases can result in privacy degradation when the same differential privacy mechanism is applied to each release. We quantify such privacy degradation in the form of temporal privacy leakages. We apply continuous event data publishing scenarios to real-life event logs to demonstrate privacy leakages.

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Privacy and Confidentiality in Process Mining: Threats and Research Challenges

Cited by 29 publications

References 47 publications

Responsible Process Mining

Responsible Process Mining

Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities

Privacy-Preserving Continuous Event Data Publishing

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