  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 paper addresses the problem of
anonymizing an event log in order to guarantee that, upon disclosure 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 paper proposes a differentially private disclosure mechanism, which
oversamples the cases in the log and adds noise to the timestamps to the extent
required to achieve the above privacy guarantee. The paper reports on an
empirical evaluation of the proposed approach using 14 real-life event logs in
terms of data utility loss and computational efficiency.
