Fine-Grained Privacy Verification via Static Data Flow Analysis
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Solution Overview
Problem
Existing privacy verification methods for mobile devices operate at a coarse level, failing to account for fine-grained data declassification rules, which leads to legitimate application behaviors being misinterpreted as privacy violations.
Innovation Solution
A system that provides fine-grained privacy specification and verification through a user interface allowing users to set privacy policies, tracking data movement, and comparing leaked data to authorized levels, using a declarative interface for policy definition and static analysis for data flow tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If coarse-level privacy verification is used, then device complexity is reduced, but measurement precision of data leakage detection deteriorates
Solution Approach 1:
The patent segments confidential data into fine-grained units (e.g., individual location coordinates, contact names, message contents) rather than treating data as coarse blocks. This segmentation enables precise tracking of specific data elements through the application, allowing the system to detect exactly what data is leaked without requiring complex overall monitoring mechanisms.
Solution Approach 2:
The patent introduces a static analysis component as an intermediary that automatically tracks data flow from confidential sources through application processing to sinks. This intermediary performs the complex verification work, freeing the user interface from complexity while maintaining high detection precision through automated data flow analysis.
2Measurement precision
If fine-grained privacy verification is implemented, then measurement precision of data leakage detection is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary static analysis of the application code to pre-identify all possible data flow paths from confidential sources to sinks. By analyzing the application structure in advance, the system creates a verification model that automatically checks data leakage against privacy policies without requiring complex runtime monitoring, thus reducing operational complexity while maintaining precision.
Solution Approach 2:
The system enables applications to self-verify their privacy compliance by embedding the verification logic within the application analysis process itself. The static analysis component automatically tracks data flow and compares it against privacy policies without external intervention, allowing the system to maintain high precision while minimizing the complexity of external verification infrastructure.
3Reliability
If fine-grained data tracking is performed, then reliability of privacy policy enforcement is improved, but loss of information about data flow increases
Solution Approach 1:
The patent extracts and tracks only the specific confidential data elements that are subject to privacy policies, rather than monitoring all data flow in the application. By selectively extracting and tracking only the relevant confidential data units, the system ensures reliable enforcement of privacy policies for protected data while minimizing information loss about unrelated data flows.
Solution Approach 2:
The patent applies different tracking intensities to different data elements based on their sensitivity and policy requirements. Highly sensitive confidential data receives fine-grained tracking with complete information retention, while less sensitive data may use coarser tracking. This local differentiation maintains high reliability for critical privacy enforcement while reducing overall information loss in the system.
Data Source
AI summary
A method includes receiving from a user via a user interface an activation of at least one element to set a privacy policy specifying the maximum amount of confidential data that is authorized to be leaked to a sink, tracking movement of confidential data through an application, determining based on the tracked movement of the confidential data that the confidential data is leaked to the sink by the application, comparing the confidential data that is leaked to the sink to the specified maximum amount of confidential data that is authorized to be leaked to the sink, and presenting to the user via the user interface an indication of whether the application complies with the privacy policy set by the user based on the comparison.