Code Changeset Privacy Risk Scoring Before Deployment
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Solution Overview
Problem
Traditional tools for detecting privacy data leaks in software are inadequate due to their limited scope, resource-intensiveness, and inability to detect complex bugs, leading to costly remediation and potential data breaches.
Innovation Solution
A system that evaluates privacy risk by analyzing code changesets using a text-based approach combined with data mining, generating a risk score based on tokenization and historical data to identify potential privacy leaks before implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pattern matching tools are used to detect privacy data leaks, then the detection process is simple to implement, but the detection accuracy and scope are limited
Solution Approach 1:
The system performs preliminary analysis of code changesets before they are merged into the main codebase. By analyzing the diff (changeset) and comparing it against historical data and privacy patterns, the system can identify potential privacy leaks early in the development cycle, preventing them from being deployed. This preliminary action enables accurate detection without requiring complex continuous monitoring of the entire codebase.
Solution Approach 2:
The system introduces an intermediary analysis layer between code development and deployment. This intermediary component (the privacy risk evaluation system) sits between the code changeset and the main codebase, performing specialized analysis using tokenization, historical data comparison, and pattern matching. This intermediary enables focused, accurate detection without complicating the entire development workflow.
2Reliability
If comprehensive code analysis is performed to detect all potential privacy leaks, then detection thoroughness improves, but computing resource consumption increases
Solution Approach 1:
The system extracts only the relevant portions of code for analysis - specifically the changeset (diff) representing recent modifications. Instead of analyzing the entire codebase, it focuses on the extracted changeset, comparing it against historical data and privacy patterns. This extraction approach maintains detection thoroughness for relevant changes while dramatically reducing computing resource consumption.
Solution Approach 2:
The analysis process is segmented into distinct phases: (1) retrieving the changeset, (2) tokenization of code terms, (3) comparison against historical data, and (4) risk score generation. This segmentation allows the system to process only necessary portions of code through each phase, improving reliability for changeset analysis while minimizing overall computing resource usage.
3Difficulty of detecting and measuring
If traditional telemetry analysis is used to detect privacy incidents, then the system can monitor runtime behavior, but it cannot detect complex software bugs that cause data leaks
Solution Approach 1:
The system performs preliminary analysis of code changesets at the development stage, before the code is deployed and executed. By analyzing the changeset structure, tokenization patterns, and historical data during the development phase, the system can identify complex bugs that would cause privacy leaks before they are deployed. This eliminates the time delay between bug occurrence and detection that exists in runtime telemetry-based approaches.
4Reliability
If extensive code review and analysis is performed to prevent privacy leaks, then security improves, but development productivity decreases
Solution Approach 1:
The system enables self-service privacy risk assessment by automatically analyzing changesets and generating risk scores without requiring manual code review for security purposes. The automated system performs tokenization, historical data comparison, and pattern matching independently, providing security assessment functionality that developers can rely on without slowing down their development workflow.
Solution Approach 2:
The system provides feedback in the form of risk scores and privacy risk assessments that developers receive automatically when submitting changesets. This feedback mechanism allows developers to understand the security implications of their changes without requiring extensive manual review, enabling them to make informed decisions quickly and maintain high development productivity while improving privacy security.
Data Source
AI summary
Privacy incidence evaluation systems and methods are disclosed herein. A code changeset is retrieved that identifies a change from a prior version of a code. A tokenization score is determined from keywords present in the code changeset, where the tokenization score is indicative of a presence of a privacy term in the code changeset. A historical score is determined based on a set of computer code stored in a code repository. A privacy risk score is generated from the tokenization score and the historical score, where the privacy risk score indicates a likelihood that the code changeset, if implemented, would result in an occurrence of a privacy incident (e.g., an unintended leak of private data). An action is performed based on the privacy risk score to mitigate a risk of a privacy incident occurrence.