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The applicability of process mining techniques hinges on the availability ofevent logs capturing the execution of a business process. In some use cases,particularly those involving customer-facing processes, these event logs maycontain private information. Data protection regulations restrict the use ofsuch event logs for analysis purposes. One way of circumventing theserestrictions is to anonymize the event log to the extent that no individual canbe singled out using the anonymized log. This paper addresses the problem ofanonymizing an event log in order to guarantee that, upon disclosure of theanonymized log, the probability that an attacker may single out any individualrepresented in the original log, does not increase by more than a threshold.The paper proposes a differentially private disclosure mechanism, whichoversamples the cases in the log and adds noise to the timestamps to the extentrequired to achieve the above privacy guarantee. The paper reports on anempirical evaluation of the proposed approach using 14 real-life event logs interms of data utility loss and computational efficiency.

Mine Me but Don't Single Me Out: Differentially Private Event Logs for Process Mining