API Privacy Relevance Classification via Taint Analysis
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Solution Overview
Problem
Mobile applications often access and handle sensitive user information without adequate privacy classification, posing concerns about data privacy.
Innovation Solution
A method and system for classifying the privacy relevance of application programming interfaces (APIs) by analyzing input applications, generating taint specifications, and matching features with privacy relevance templates to identify and label candidate APIs based on their privacy relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If mobile applications access and handle sensitive user information, then application functionality and user service quality are improved, but data privacy security deteriorates
Solution Approach 1:
The system performs preliminary classification of APIs into privacy-relevant and privacy-irrelevant categories before the application actually accesses sensitive data. By pre-identifying which APIs may handle sensitive information and establishing their classification in advance, the system enables proactive privacy management rather than reactive responses after data exposure occurs.
Solution Approach 2:
The patent introduces an intermediary classification system that acts as a mediator between the application's data access operations and the privacy protection mechanism. This intermediary layer analyzes API characteristics, matches them against privacy templates, and determines privacy relevance without directly interfering with the application's core functionality, thus balancing operational needs with privacy protection.
2Reliability
If comprehensive privacy monitoring of all APIs is implemented, then data privacy protection is improved, but system complexity and processing overhead increase
Solution Approach 1:
The patent segments the API monitoring task by dividing APIs into distinct categories: privacy-relevant APIs and privacy-irrelevant APIs. This segmentation is achieved by analyzing API characteristics and matching them against privacy templates. By segmenting the monitoring focus, the system avoids the need to uniformly monitor all APIs with the same level of scrutiny, thereby reducing overall system complexity while maintaining comprehensive privacy protection for relevant APIs.
Solution Approach 2:
The system applies local quality by differentiating the level of monitoring and classification applied to different APIs based on their privacy relevance. Privacy-relevant APIs receive detailed analysis and template matching, while privacy-irrelevant APIs are either excluded from monitoring or receive minimal processing. This localized approach to quality ensures resources are concentrated where privacy protection is most needed, reducing unnecessary processing overhead.
3Measurement precision
If manual classification of each API for privacy relevance is performed, then classification accuracy is improved, but processing time and operational effort increase
Solution Approach 1:
The system enables self-service classification by automatically analyzing API characteristics, comparing them against privacy templates, and determining privacy relevance without requiring manual intervention. The automated classification process extracts features from API definitions, matches them with predefined privacy templates, and generates classifications autonomously, thereby maintaining high accuracy while eliminating the time loss associated with manual classification of each API.
Solution Approach 2:
The patent transforms the classification problem from a manual, time-consuming process into an automated parameter-matching process. By defining privacy relevance in terms of specific parameters and features that can be automatically extracted and compared against templates, the system changes the nature of the classification task from qualitative manual judgment to quantitative automated parameter matching, significantly reducing processing time while maintaining or improving accuracy.
Data Source
AI summary
A method of classifying privacy relevance of an application programming interface (API) comprises analyzing a set of input applications to identify a plurality of custom APIs and generating a respective taint specification for each identified custom API. The method further comprises generating taint flows based on each taint specification and matching features and associated feature values from the taint flows to a set of feature templates. The method also comprises correlating the matched features and associated feature values with respective privacy relevance of the plurality of custom APIs to identify a set of privacy relevant features. The method further comprises detecting a candidate API, extracting features from the candidate API and comparing the extracted features to the set of privacy relevant features. Based on the comparison, a label is assigned to the candidate API indicating privacy relevance of the candidate API.