  System logs constitute valuable information for analysis and diagnosis of
system behavior. The size of parallel computing systems and the number of their
components steadily increase. The volume of generated logs by the system is in
proportion to this increase. Hence, long-term collection and storage of system
logs is challenging. The analysis of system logs requires advanced text
processing techniques. For very large volumes of logs, the analysis is highly
time-consuming and requires a high level of expertise. For many parallel
computing centers, outsourcing the analysis of system logs to third parties is
the only affordable option. The existence of sensitive data within system log
entries obstructs, however, the transmission of system logs to third parties.
Moreover, the analytical tools for processing system logs and the solutions
provided by such tools are highly system specific. Achieving a more general
solution is only possible through the access and analysis system of logs of
multiple computing systems. The privacy concerns impede, however, the sharing
of system logs across institutions as well as in the public domain. This work
proposes a new method for the anonymization of the information within system
logs that employs de-identification and encoding to provide sharable system
logs, with the highest possible data quality and of reduced size. The results
presented in this work indicate that apart from eliminating the sensitive data
within system logs and converting them into shareable data, the proposed
anonymization method provides 25% performance improvement in post-processing of
the anonymized system logs, and more than 50% reduction in their required
storage space.
