Child-Directed App Privacy Risk Classification Using Permission Signals
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Solution Overview
Problem
Existing technologies struggle to accurately identify child-directed applications and assess their privacy risks, as many applications collect and disclose personal information from children without proper parental consent, violating regulations like COPPA and GDPR-K.
Innovation Solution
A system and method to determine if an application is directed at children and assess its privacy risk by analyzing application information, including permissions, location data, and compliance with regulations, using a combination of signals and machine learning to categorize privacy risk levels as low, medium, or high.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If applications collect personal information from children without verification, then data collection efficiency is improved, but privacy risk and regulatory compliance deteriorate
Solution Approach 1:
The system performs preliminary classification of applications as child-directed or not before data collection occurs. By analyzing app metadata, content, and permissions in advance, the system establishes a risk baseline that enables subsequent privacy-protective measures to be applied only where necessary, maintaining efficiency while ensuring compliance.
Solution Approach 2:
The patent introduces an intermediary classification system between the application and the data collection process. This intermediary layer analyzes application characteristics and determines child-directed status, acting as a mediator that enables targeted privacy protections without blocking legitimate data collection from non-child users.
2Measurement precision
If comprehensive privacy assessment is performed on all applications, then privacy protection accuracy is improved, but system complexity and processing time worsen
Solution Approach 1:
The patent segments the privacy assessment process into distinct classification stages. First, applications are classified as child-directed or not based on metadata and content analysis. Second, separate privacy risk assessment is performed only on child-directed applications. This segmentation reduces overall system complexity by avoiding comprehensive assessment of all applications while maintaining high accuracy for the target group.
Solution Approach 2:
The system applies different levels of assessment intensity to different applications based on their classification. Child-directed applications receive comprehensive privacy assessment with multiple signals analyzed, while non-child-directed applications receive minimal or no assessment. This local quality approach optimizes resource allocation and reduces overall system complexity while maintaining high accuracy where it matters most.
3Measurement precision
If multiple signals are analyzed to determine child-directed status, then classification accuracy is improved, but processing time and computational resources worsen
Solution Approach 1:
The system performs preliminary analysis of easily obtainable signals first, such as application metadata, category, and explicit age targeting information. Based on these preliminary results, the system can quickly classify many applications without needing to analyze all signals, reducing processing time while maintaining accuracy through a staged approach.
Solution Approach 2:
The patent implements a partial action strategy where not all possible signals are analyzed for every application. Instead, the system analyzes a subset of signals sufficient to achieve accurate classification in most cases, avoiding the computational overhead of comprehensive multi-signal analysis for every application while maintaining high classification accuracy.
Data Source
AI summary
Non-limiting, non-exclusive aspects of the present disclosure relate to determining the user group to which an application is directed, and the level of user group privacy risks associated with the application. In particular, the disclosed technology receives, detects and processes application information, determines if the application is directed at a group of users (e.g., children), and, if the application is indeed directed at the target user group, the disclosed technology generates a user group privacy risk level derived from application information signals. Application information signals may include a determination of whether the application requires permissions that enable access to or capture of personal information from or related to the application's users such as residential end-user IP address or precise location information. The privacy risk assessment may be based on signals associated with applicable privacy regulations (e.g., the rule implementing the Children's Online Privacy Protection Act (COPPA)).