SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining

Privacy-preserving process mining enables the analysis of business processes using event logs, while giving guarantees on the protection of sensitive information on process stakeholders. To this end, existing approaches add noise to the results of queries that extract properties of an event log, such as the frequency distribution of trace variants, for analysis.Noise insertion neglects the semantics of the process, though, and may generate traces not present in the original log. This is problematic. It lowers the utility of the published data and makes noise easily identifiable, as some traces will violate well-known semantic constraints.In this paper, we therefore argue for privacy preservation that incorporates a process semantics. For common trace-variant queries, we show how, based on the exponential mechanism, semantic constraints are incorporated to ensure differential privacy of the query result. Experiments demonstrate that our semantics-aware anonymization yields event logs of significantly higher utility than existing approaches.

A Distance Measure for Privacy-preserving Process Mining based on Feature Learning

To enable process analysis based on an event log without compromising the privacy of individuals involved in process execution, a log may be anonymized. Such anonymization strives to transform a log so that it satisfies provable privacy guarantees, while largely maintaining its utility for process analysis. Existing techniques perform anonymization using simple, syntactic measures to identify suitable transformation operations. This way, the semantics of the activities referenced by the events in a trace are neglected, potentially leading to transformations in which events of unrelated activities are merged. To avoid this and incorporate the semantics of activities during anonymization, we propose to instead incorporate a distance measure based on feature learning. Specifically, we show how embeddings of events enable the definition of a distance measure for traces to guide event log anonymization. Our experiments with real-world data indicate that anonymization using this measure, compared to a syntactic one, yields logs that are closer to the original log in various dimensions and, hence, have higher utility for process analysis.

Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring

Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood that it will lead to an undesired outcome.These techniques, however, focus on generating predictions and do not prescribe when and how process workers should intervene to decrease the cost of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive monitoring with the ability to generate alarms that trigger interventions to prevent an undesired outcome or mitigate its effect. The framework incorporates a parameterized cost model to assess the cost-benefit trade-off of generating alarms. We show how to optimize the generation of alarms given an event log of past process executions and a set of cost model parameters. The proposed approaches are empirically evaluated using a range of real-life event logs. The experimental results show that the net cost of undesired outcomes can be minimized by changing the threshold for generating alarms, as the process instance progresses. Moreover, introducing delays for triggering alarms, instead of triggering them as soon as the probability of an undesired outcome exceeds a threshold, leads to lower net costs.