Assessing Data Usefulness for Failure Analysis in Anonymized System Logs

System logs are a valuable source of information for the analysis and understanding of systems behavior for the purpose of improving their performance. Such logs contain various types of information, including sensitive information. Information deemed sensitive can either directly be extracted from system log entries by correlation of several log entries, or can be inferred from the combination of the (non-sensitive) information contained within system logs with other logs and/or additional datasets. The analysis of system logs containing sensitive information compromises data privacy. Therefore, various anonymization techniques, such as generalization and suppression have been employed, over the years, by data and computing centers to protect the privacy of their users, their data, and the system as a whole. Privacy-preserving data resulting from anonymization via generalization and suppression may lead to significantly decreased data usefulness, thus, hindering the intended analysis for understanding the system behavior. Maintaining a balance between data usefulness and privacy preservation, therefore, remains an open and important challenge. Irreversible encoding of system logs using collision-resistant hashing algorithms, such as SHAKE-128, is a novel approach previously introduced by the authors to mitigate data privacy concerns. The present work describes a study of the applicability of the encoding approach from earlier work on the system logs of a production high performance computing system. Moreover, a metric is introduced to assess the data usefulness of the anonymized system logs to detect and identify the failures encountered in the system.Comment: 11 pages, 3 figures, submitted to 17th IEEE International Symposium on Parallel and Distributed Computin

α-MON: Traffic Anonymizer for Passive Monitoring

Packet measurements at scale are essential for several applications, such as cyber-security, accounting and troubleshooting. They, however, threaten users’ privacy by exposing sensitive information. Anonymization has been the answer to this challenge, i.e., replacing sensitive information with obfuscated copies. Anonymization of packet traces, however, comes with some challenges and drawbacks. First, it reduces the value of data. Second, it requires to consider diverse protocols because information may leak from many non-encrypted fields. Third, it must be performed at high speeds directly at the monitor, to prevent private data from leaking, calling for real-time solutions. We present , a flexible tool for privacy-preserving packet monitoring. It replicates input packet streams to different consumers while anonymizing protocol fields according to flexible policies that cover all protocol layers. Beside classic anonymization mechanisms such as IP address obfuscation, supports z-anonymization, a novel solution to obfuscate rare values that can be uniquely traced back to limited sets of users. Differently from classic anonymization approaches, works on a streaming fashion, with zero delay, operating at high-speed links on a packet-by-packet basis. We quantify the impact of on traffic measurements, finding that it introduces minimal error when it comes to finding heavy-hitter services. We evaluate performance using packet traces collected from an ISP network and show that it achieves a sustainable rate of 40 Gbit/s on a Commercial Off-the Shelf server. is available to the community as an open-source project