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In many software applications, logs serve as the only interface between theapplication and the developer. However, navigating through the logs oflong-running applications is often challenging. Logs from previously successfulapplication runs can be leveraged to automatically identify errors and provideusers with only the logs that are relevant to the debugging process. Wedescribe a privacy preserving framework which can be employed by Platform as aService (PaaS) providers to utilize the user logs generated on the platformwhile protecting the potentially sensitive logged data. Further, in order toaccurately and scalably parse log lines, we present a distributed log parsingalgorithm which leverages Locality Sensitive Hashing (LSH). We outperform thestate-of-the-art on multiple datasets. We further demonstrate the scalabilityof Delog on publicly available Thunderbird log dataset with close to 27,000unique patterns and 211 million lines.

Delog: A Privacy Preserving Log Filtering Framework for Online Compute Platforms