Proactive Privacy Content Hosting for Media Sharing
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Solution Overview
Problem
The sharing of media items on online services poses risks such as the collection of private information and exposure to nefarious third parties, while regulations like GDPR restrict data access, leading to user dissatisfaction and potential withdrawal from these services.
Innovation Solution
Proactive Private Content Hosting (PPCH) uses machine learning and artificial neural networks to analyze and alter media items, identifying and modifying sensitive information before access, ensuring privacy and compliance with regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If media items are hosted on remote servers for sharing, then accessibility and convenience are improved, but privacy security and control over sensitive information deteriorate
Solution Approach 1:
The system performs content analysis and generates altered media items before they are accessed or shared. By proactively identifying sensitive information and creating modified versions in advance, the system ensures privacy protection is already in place when media items are shared, eliminating the need for users to manually review or redact sensitive content before posting.
Solution Approach 2:
The system introduces an intermediary processing layer between the original media items and the shared content. This intermediary automatically analyzes media items, identifies sensitive information using content analysis, and generates altered versions that protect privacy while maintaining sharing functionality. Users interact with this intermediary system rather than directly managing privacy controls for each media item.
2Object-affected harmful factors
If data access is restricted to comply with regulations like GDPR, then privacy protection is improved, but user satisfaction and service continuity deteriorate
Solution Approach 1:
The system changes the parameters of media items by generating altered versions with modified sensitive information while preserving the overall content and utility. This allows the same media item to satisfy both privacy protection requirements and service accessibility needs, as the altered parameters maintain functionality while removing harmful data elements.
Solution Approach 2:
The system segments media items by separating sensitive information from non-sensitive content. Through content analysis, it identifies and isolates specific sensitive elements, then creates altered versions that retain the valuable non-sensitive portions while protecting the segmented sensitive information, thereby maintaining service productivity while ensuring compliance.
3Object-affected harmful factors
If content analysis is performed on media items, then privacy protection is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs content analysis as a preliminary action when media items are first uploaded or stored, rather than waiting for access requests. This upfront analysis generates altered media items in advance, so when users or systems request access, the protective alterations are already in place, significantly reducing the time loss at access points.
Solution Approach 2:
The system implements self-service content analysis that automatically processes media items without requiring manual review or intervention. The automated content analysis system independently identifies sensitive information and generates altered versions, eliminating the need for time-consuming human review while maintaining effective privacy protection.
Data Source
AI summary
A set of one or more media items is identified by a first computer system configured to host media items for various users. The set of media items has a first relationship. A content analysis is performed on the set of one or more media items. The content analysis is based on a first machine-learning model. A first content pattern contained within the set of media items is determined based on the content analysis. A first set of one or more altered media items is generated in response to the first content pattern.