  System logs are a common source of monitoring data for analyzing computing
systems' behavior. Due to the complexity of modern computing systems and the
large size of collected monitoring data, automated analysis mechanisms are
required. Numerous machine learning and deep learning methods are proposed to
address this challenge. However, due to the existence of sensitive data in
system logs their analysis and storage raise serious privacy concerns.
Anonymization methods could be used to clean the monitoring data before
analysis. However, anonymized system logs, in general, do not provide adequate
usefulness for the majority of behavioral analysis. Content-aware anonymization
mechanisms such as PaRS preserve the correlation of system logs even after
anonymization. This work evaluates the usefulness of anonymized system logs
taken from the Taurus HPC cluster anonymized using PaRS, for behavioral
analysis via recurrent neural network models.
