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Pseudonymisation provides the means to reduce the privacy impact ofmonitoring, auditing, intrusion detection, and data collection in general onindividual subjects. Its application on data records, especially in anenvironment with additional constraints, like re-identification in the courseof incident response, implies assumptions and privacy issues, which contradictthe achievement of the desirable privacy level. Proceeding from two real-worldscenarios, where personal and identifying data needs to be processed, weidentify requirements as well as a system model for pseudonymisation andexplicitly state the sustained privacy threats, even when pseudonymisation isapplied. With this system and threat model, we derive privacy protection goalstogether with possible technical realisations, which are implemented andintegrated into our event pseudonymisation framework PEEPLL for the context ofevent processing, like monitoring and auditing of user, process, and networkactivities. Our framework provides privacy-friendly linkability in order tomaintain the possibility for automatic event correlation and evaluation, whileat the same time reduces the privacy impact on individuals. Additionally, thepseudonymisation framework is evaluated in order to provide some restrainedinsights on the impact of assigned paradigms and all necessary new mechanismson the performance of monitoring and auditing. With this framework, privacyprovided by event pseudonymisation can be enhanced by a more rigorouscommitment to the concept of personal data minimisation, especially in thecontext of regulatory requirements like the European General Data ProtectionRegulation.

PEEPLL: Privacy-Enhanced Event Pseudonymisation with Limited Linkability