Video Security System Segment Access Control
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Solution Overview
Problem
Current video content security systems lack the granularity to protect specific segments of videos, only offering table-level and row-level authorization, failing to enforce access controls on video data within videos, and there is a need for automated security enforcement using artificial intelligence and machine learning.
Innovation Solution
A system that dynamically identifies and secures video segments with enhanced security requirements by associating access control lists with video metadata, comparing user access levels to segment access requirements, and creating playlists that exclude unauthorized segments, replacing them with filler content if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If video content security systems secure access to an entire video or collection of videos, then the security coverage is comprehensive, but the granularity of protection is insufficient and cannot protect specific segments within the video
Solution Approach 1:
The video content is divided into discrete segments or clips, each with its own access control metadata. This segmentation allows different portions of the video to have different security requirements, enabling fine-grained control over what users can access while maintaining system manageability through modular organization of access rights.
Solution Approach 2:
The system introduces a temporal dimension to access control by associating specific time ranges with video segments. Instead of treating video as a single static entity, the system creates a time-based dimension where different segments at different time ranges can have different access control lists, enabling precise control over when and what users can view.
2Measurement precision
If manual methods are used to identify and tag protected segments in videos, then the security enforcement is accurate, but the process is time-consuming and not scalable
Solution Approach 1:
The system performs preliminary analysis of video content to automatically identify segments containing sensitive information before users request the video. By pre-tagging segments with access control metadata during video upload or processing, the system eliminates the need for real-time manual review while maintaining accurate security enforcement when videos are accessed.
Solution Approach 2:
The system employs artificial intelligence and machine learning models that automatically analyze video content, identify protected segments, and generate appropriate access control metadata without human intervention. This self-service capability enables the system to handle large volumes of video content efficiently while maintaining consistent and accurate security tagging across all videos.
3Ease of operation
If all video segments are provided to users with full access rights, then the user experience is seamless, but unauthorized access to sensitive content cannot be prevented
Solution Approach 1:
The system introduces a server as an intermediary between the video storage and the user's playback device. This intermediary dynamically generates playlists by filtering video segments based on the user's access rights, seamlessly replacing unauthorized segments with filler content. This approach maintains user experience continuity while reliably enforcing security controls without requiring complex client-side authentication logic.
Solution Approach 2:
The system dynamically changes the playlist parameters (which video segments are included) based on the user's access level. By modifying the composition of the playlist in real-time according to user credentials and segment access control lists, the system seamlessly adapts the delivered content to match security requirements while maintaining uninterrupted playback for authorized content.
4Manufacturing precision
If fine-grained access control is implemented at the video segment level, then security precision is improved, but the system complexity and computational requirements increase significantly
Solution Approach 1:
The video is segmented into smaller clips with individual access control metadata, allowing precise control over which portions users can access. This segmentation enables high precision in access control while keeping each segment manageable in size and complexity, making the overall system more tractable than attempting to manage entire videos as single units.
Data Source
AI summary
A system and method to tag portions of a video with an access-control-list (ACL) for those periods of time (e.g., video segments, series of video frames, etc.) within a video that require elevated permission to view or access is provided. At playback time, the access-control-list is dynamically enforced in real-time to ensure that a user viewing or editing the video has permissions to view upcoming portions of the video. When a user has insufficient permission to view a portion of the video, a filler frame, blurred content or blank frame is displayed in the place of the actual video content. Audio may also be muted or beeped out during the periods of the video for which there is insufficient permissions.