Detecting Privacy-Sensitive Code Changes with Language Modeling

Abstract

At Meta, we work to incorporate privacy-by-design into all of our products and keep user information secure. We have created an ML model that detects code changes ('diffs') that have privacy-sensitive implications. At our scale of tens of thousands of engineers creating hundreds of thousands of diffs each month, we use automated tools for detecting such diffs. Inspired by recent studies on detecting defects [2], [3], [5] and security vulnerabilities [4], [6], [7], we use techniques from natural language processing to build a deep learning system for detecting privacy-sensitive code.