Knowledge & Learning-based Adaptable System for Sensitive Information Identification and Handling

TL;DR

KLASSIFI is an adaptable system that identifies and redacts sensitive information in diagnostic data, ensuring data privacy while maintaining utility, with proven scalability and performance in large file processing.

Contribution

The paper introduces KLASSIFI, a customizable, end-to-end system for sensitive information handling in diagnostic data, with optimized performance and scalability.

Findings

Processes 128 GB files in 84 minutes

Performance scales linearly with data size

Maintains metadata for debugging tools

Abstract

Diagnostic data such as logs and memory dumps from production systems are often shared with development teams to do root cause analysis of system crashes. Invariably such diagnostic data contains sensitive information and sharing it can lead to data leaks. To handle this problem we present Knowledge and Learning-based Adaptable System for Sensitive InFormation Identification and Handling (KLASSIFI) which is an end to end system capable of identifying and redacting sensitive information present in diagnostic data. KLASSIFI is highly customizable, allowing it to be used for various different business use cases by simply changing the configuration. KLASSIFI ensures that the output file is useful by retaining the metadata which is used by various debugging tools. Various optimizations have been done to improve the performance of KLASSIFI. Empirical evaluation of KLASSIFI shows that it is able to process large files (128 GB) in 84 minutes and its performance scales linearly with varying factors. This points to practicability of KLASSIFI