PEEPLL: Privacy-Enhanced Event Pseudonymisation with Limited Linkability
Ephraim Zimmer, Christian Burkert, Tom Petersen, Hannes Federrath

TL;DR
PEEPLL introduces a privacy-enhanced event pseudonymisation framework that balances event linkability for analysis with privacy protection, addressing real-world data processing and regulatory compliance challenges.
Contribution
The paper presents a novel pseudonymisation system model and implementation that enables privacy-preserving event correlation in monitoring and auditing environments.
Findings
Framework maintains event correlation with reduced privacy risks
Implementation demonstrates acceptable performance impact
Enhances compliance with GDPR requirements
Abstract
Pseudonymisation provides the means to reduce the privacy impact of monitoring, auditing, intrusion detection, and data collection in general on individual subjects. Its application on data records, especially in an environment with additional constraints, like re-identification in the course of incident response, implies assumptions and privacy issues, which contradict the achievement of the desirable privacy level. Proceeding from two real-world scenarios, where personal and identifying data needs to be processed, we identify requirements as well as a system model for pseudonymisation and explicitly state the sustained privacy threats, even when pseudonymisation is applied. With this system and threat model, we derive privacy protection goals together with possible technical realisations, which are implemented and integrated into our event pseudonymisation framework PEEPLL for the…
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