Quantifying Temporal Privacy Leakage in Continuous Event Data Publishing

TL;DR

This paper investigates how continuous publishing of event data in process mining can lead to increased privacy risks due to correlations over time, quantifying the resulting privacy leakages.

Contribution

It introduces a method to quantify temporal privacy leakage in continuous event data publishing, highlighting privacy degradation overlooked by traditional differential privacy applications.

Findings

Continuous data releases are correlated, increasing privacy risks.

Correlation among releases can cause privacy degradation.

Real-life event logs demonstrate significant privacy leakages.

Abstract

Process mining employs event data extracted from different types of information systems to discover and analyze actual processes. Event data often contain highly sensitive information about the people who carry out activities or the people for whom activities are performed. Therefore, privacy concerns in process mining are receiving increasing attention. To alleviate privacy-related risks, several privacy preservation techniques have been proposed. Differential privacy is one of these techniques which provides strong privacy guarantees. However, the proposed techniques presume that event data are released in only one shot, whereas business processes are continuously executed. Hence, event data are published repeatedly, resulting in additional risks. In this paper, we demonstrate that continuously released event data are not independent, and the correlation among different releases can result in privacy degradation when the same differential privacy mechanism is applied to each release. We quantify such privacy degradation in the form of temporal privacy leakages. We apply continuous event data publishing scenarios to real-life event logs to demonstrate privacy leakages.