AR/VR Privacy Sandbox for Real-Time Private Space Anonymization
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Solution Overview
Problem
Existing augmented reality (AR) and virtual reality (VR) systems fail to effectively preserve user privacy by inadvertently capturing and sharing private information, such as images and audio, in public spaces.
Innovation Solution
Implementing a secure data vault system with generative AI models that process raw data within a sandbox environment to detect and obfuscate private information in real-time, using a secure data vault system to isolate and manage data usage, and employing a sandbox environment to execute generative AI models, thereby preventing unauthorized access and enhancing privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AR/VR systems capture and share media content in public spaces, then user interaction and content sharing capabilities are improved, but user privacy and security are compromised due to inadvertent capture of private information
Solution Approach 1:
The system performs preliminary classification of media content into public and private spaces before sharing. AI models analyze the captured content in real-time to identify private information (personally identifiable information, sensitive locations, private conversations) and prevent its dissemination. This preliminary action ensures privacy protection is built-in before content is shared, resolving the contradiction between content sharing capability and privacy violation.
2Reliability
If real-time processing of media content is implemented to detect private information, then privacy preservation is improved, but system complexity and processing time increase
Solution Approach 1:
The system segments the media content processing into distinct components: image analysis, audio analysis, text analysis, and classification modules. Each component focuses on specific aspects of private information detection. This segmentation allows parallel processing of different content types, reducing overall system complexity while maintaining comprehensive privacy preservation through specialized sub-systems working together.
3Measurement precision
If comprehensive AI models are used to detect all types of private information, then detection accuracy is improved, but processing speed and energy consumption increase
Solution Approach 1:
The system applies different levels of analysis intensity to different types of content based on their privacy risk. High-risk content (e.g., clearly identifiable personal information, sensitive locations) receives comprehensive AI analysis, while low-risk content receives lighter processing. This local quality approach ensures high detection accuracy for critical privacy threats while maintaining processing speed for routine content, resolving the contradiction between detection accuracy and processing speed.
Data Source
AI summary
Systems and methods are included. A system includes a display, a camera, and a secure data vault system. The secure data vault system includes a sandbox system operatively coupled to the camera and configured to receive camera data from the camera, wherein in operation of the sandbox system, the camera only sends camera data to the sandbox system, and wherein the sandbox system comprises an execution environment configured to restrict execution of instructions to a predefined memory address range. The secure data vault system also includes a display and rending system operatively coupled to the sandbox system and configured to render an image based on the camera data processed via the instructions and to display the image via the display, wherein the display and rendering system is configured to blur sections of the image based on location information derived from the image.