Differentially Private Release of Event Logs for Process Mining
Gamal Elkoumy, Alisa Pankova, Marlon Dumas

TL;DR
This paper introduces a differentially private method for anonymizing event logs used in process mining, balancing privacy guarantees with data utility, and compares it empirically to existing approaches.
Contribution
It proposes a novel differentially private release mechanism for event logs, adding noise and sampling to ensure privacy without significantly compromising data utility.
Findings
The proposed method maintains privacy guarantees effectively.
It outperforms state-of-the-art approaches in utility preservation.
The approach is computationally efficient across multiple real-life logs.
Abstract
The applicability of process mining techniques hinges on the availability of event logs capturing the execution of a business process. In some use cases, particularly those involving customer-facing processes, these event logs may contain private information. Data protection regulations restrict the use of such event logs for analysis purposes. One way of circumventing these restrictions is to anonymize the event log to the extent that no individual can be singled out using the anonymized log. This article addresses the problem of anonymizing an event log in order to guarantee that, upon release of the anonymized log, the probability that an attacker may single out any individual represented in the original log does not increase by more than a threshold. The article proposes a differentially private release mechanism, which samples the cases in the log and adds noise to the timestamps…
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Taxonomy
TopicsBusiness Process Modeling and Analysis · Digitalization, Law, and Regulation · Information and Cyber Security
