# Turning Privacy Constraints into Syslog Analysis Advantage

## TL;DR

This paper presents a method that transforms privacy constraints in syslog analysis into an advantage, enabling efficient failure detection in HPC systems while protecting user privacy.

## Contribution

It introduces a privacy-preserving syslog analysis approach that reduces storage and processing time, turning privacy constraints into an analytical benefit.

## Key findings

- Significant reduction in storage space needed for syslogs
- Processing time is three times shorter
- Effective early failure detection in HPC systems

## Abstract

The mean time between failures (MTBF) of HPC systems is rapidly reducing, and that current failure recovery mechanisms e.g., checkpoint-restart, will no longer be able to recover the systems from failures. Early failure detection is a new class of failure recovery methods that can be beneficial for HPC systems with short MTBF. System logs (syslogs) are invaluable source of information which give us a deep insight about system behavior, and make the early failure detection possible. Beside normal information, syslogs contain sensitive data which might endanger users' privacy. Even though analyzing various syslogs is necessary for creating a general failure detection/prediction method, privacy concerns discourage system administrators to publish syslogs. Herein, we ensure user privacy via de-identifying syslogs, and then turning the applied constraint for addressing users' privacy into an advantage for system behavior analysis. Results indicate significant reduction in required storage space and 3 times shorter processing time.

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Source: https://tomesphere.com/paper/1901.06918