Private Content Classification for Secure Folder Transfer
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Solution Overview
Problem
Existing methods require manual selection and transfer of private content to secure folders, which is time-consuming and inconvenient, and do not effectively distinguish between globally and user-specific objectionable content.
Innovation Solution
An electronic device uses machine learning to automatically detect and categorize private content based on the presence of private body parts or objectionable elements, and switches between secure and non-secure folders with user authentication, recommending actions such as transfer to the secure folder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual selection and transfer of private content to secure folder is implemented, then privacy protection is achieved, but time consumption and operational inconvenience increase
Solution Approach 1:
The system automatically detects and categorizes private content without requiring user intervention. The electronic device performs self-service by using machine learning models to identify private body parts, objectionable elements, and sensitive information, then automatically transfers detected private content to the secure folder, eliminating manual selection and transfer operations
Solution Approach 2:
The system performs preliminary classification of content as private or public before sharing operations occur. By pre-categorizing content and pre-transferring identified private content to secure folders, the system prepares the environment in advance to prevent accidental sharing, rather than requiring manual intervention at the moment of sharing
2Productivity
If machine learning model is used to automatically detect private content, then time efficiency is improved, but device complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component between the camera/gallery application and the secure folder system. This ML model acts as a mediator that automatically analyzes content, identifies private elements, and triggers transfer operations, thereby managing the complexity internally while presenting a simple automatic classification interface to users
Solution Approach 2:
The system segments the content analysis task into distinct ML model components that detect different types of private content (private body parts, objectionable elements, sensitive information). This segmentation allows the complex detection task to be divided into specialized sub-tasks, managing overall system complexity through modular architecture
3Reliability
If secure folder mechanism is implemented, then content privacy is protected, but ease of operation deteriorates due to authentication requirements
Solution Approach 1:
The system automatically manages content transfer to and from secure folders without requiring repeated user authentication. Once the secure folder is initially set up with authentication, the system performs self-service by automatically detecting private content and transferring it to the secure folder, and automatically retrieving public content when needed, eliminating the need for manual authentication operations
Solution Approach 2:
Authentication is performed preliminarily when the secure folder is created and configured. This preliminary authentication establishes the security mechanism in advance, allowing subsequent automatic operations to proceed without requiring repeated authentication, thereby improving ease of operation while maintaining privacy protection
Data Source
AI summary
Embodiments herein provide a method for managing private content by an electronic device (100). The method includes: displaying content on a screen of the electronic device (100), wherein the content is stored in a non-secure folder of the electronic device (100) and extracting a feature(s) of the displayed content to categorize the displayed content using a machine learning (ML) model. Further, the method includes categorizing the displayed content into the private content or public content based on globally objectionable private content and user-specific objectionable private content. Further, the method includes displaying a recommendation on the screen of the electronic device (100) for transferring the displayed content into a secure folder of the electronic device (100), detecting an event to transfer the private content into the secure folder of the electronic device (100), and automatically transferring the private content from the non-secure folder to the secure folder of the electronic device (100).