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Process mining techniques enable analysts to identify and assess processimprovement opportunities based on event logs. A common roadblock to processmining is that event logs may contain private information that cannot be usedfor analysis without consent. An approach to overcome this roadblock is toanonymize the event log so that no individual represented in the original logcan be singled out based on the anonymized one. Differential privacy is ananonymization approach that provides this guarantee. A differentially privateevent log anonymization technique seeks to produce an anonymized log that is assimilar as possible to the original one (high utility) while providing arequired privacy guarantee. Existing event log anonymization techniques operateby injecting noise into the traces in the log (e.g., duplicating, perturbing,or filtering out some traces). Recent work on differential privacy has shownthat a better privacy-utility tradeoff can be achieved by applying subsamplingprior to noise injection. In other words, subsampling amplifies privacy. Thispaper proposes an event log anonymization approach called Libra that exploitsthis observation. Libra extracts multiple samples of traces from a log,independently injects noise, retains statistically relevant traces from eachsample, and composes the samples to produce a differentially private log. Anempirical evaluation shows that the proposed approach leads to a considerablyhigher utility for equivalent privacy guarantees relative to existingbaselines.

Libra: High-Utility Anonymization of Event Logs for Process Mining via Subsampling