Contextual Content Masking for Secure Display Sharing
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Solution Overview
Problem
User devices struggle to effectively mask private content when sharing displays, as existing technologies lack efficient methods to automatically identify and separate private from non-private content based on contextual data, leading to potential exposure of sensitive information.
Innovation Solution
Implementing cognitive computing techniques to automatically identify private content on user devices by analyzing contextual data, such as user profiles and natural language classification, and generating instructions to mask private portions while keeping non-private portions visible, allowing users to share displays securely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual masking of private content is implemented, then user privacy is protected, but user operation complexity increases and productivity decreases
Solution Approach 1:
The system automatically identifies and masks private content without requiring user intervention. The device performs self-service by analyzing contextual data, determining privacy sensitivity, and applying masking instructions autonomously, thereby protecting privacy while maintaining sharing efficiency.
Solution Approach 2:
The system performs preliminary analysis of content to identify private portions before sharing occurs. By pre-identifying and masking sensitive information based on contextual data analysis, the system prepares the content in advance, eliminating the need for manual masking operations during the sharing process.
2Productivity
If automatic identification of private content is implemented, then productivity improves, but device complexity increases
Solution Approach 1:
The system introduces an intermediary layer that analyzes contextual data associated with content. This intermediary component evaluates the context to determine privacy sensitivity and generates appropriate masking instructions, enabling automatic identification without requiring complex user-defined rules or manual configuration.
3Measurement precision
If contextual analysis is performed to identify private content, then measurement precision improves, but use of energy increases
Solution Approach 1:
The system performs contextual analysis selectively rather than analyzing all content uniformly. It focuses computational resources on evaluating contextual data for portions of content that are likely to contain private information based on initial assessment, thereby achieving accurate privacy identification while reducing overall energy consumption.
Data Source
AI summary
A computer-implemented method includes: identifying, by a computing device, private portions and non-private portions of content displayed on a user device; generating, by the computing device, instructions to modify the display of the content on the user device to mask the private portions of the content, group the private portions of the content together, and group and the non-private portions of the content together; and outputting, by the computing device, the instructions to cause the user device to modify the display of the content on the user device such that the masked private portions of the content are grouped together and the non-private portions of the content are grouped together, wherein the non-private portions are exposed and visible.