PRIPEL: Privacy-Preserving Event Log Publishing Including Contextual Information

TL;DR

PRIPEL is a framework that enables privacy-preserving publication of event logs, including contextual information, by focusing on individual cases to support detailed process analysis while protecting sensitive data.

Contribution

It introduces a novel approach that preserves contextual details and long tail behaviors by ensuring privacy at the case level, unlike existing methods.

Findings

Successful case study with real-world event log

Preserves contextual information for detailed analysis

Ensures privacy at the individual case level

Abstract

Event logs capture the execution of business processes in terms of executed activities and their execution context. Since logs contain potentially sensitive information about the individuals involved in the process, they should be pre-processed before being published to preserve the individuals' privacy. However, existing techniques for such pre-processing are limited to a process' control-flow and neglect contextual information, such as attribute values and durations. This thus precludes any form of process analysis that involves contextual factors. To bridge this gap, we introduce PRIPEL, a framework for privacy-aware event log publishing. Compared to existing work, PRIPEL takes a fundamentally different angle and ensures privacy on the level of individual cases instead of the complete log. This way, contextual information as well as the long tail process behaviour are preserved, which enables the application of a rich set of process analysis techniques. We demonstrate the feasibility of our framework in a case study with a real-world event log.