Social Network Privacy Management via Normalized Indices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Social networking platforms inadequately protect sensitive user information due to coarse privacy settings and complexity, leading to potential exposure of personal data.
Innovation Solution
A method and system for managing privacy settings in social networks using normalized privacy indices, calculated from attribute scores and user relationship distance values, which recommend and implement more secure settings based on user preferences and relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If coarse privacy settings preferences are established for social network users, then ease of operation is improved, but manufacturing precision (privacy protection accuracy) deteriorates
Solution Approach 1:
The patent segments privacy settings into multiple granularity levels: coarse-grained preference categories (e.g., 'friends', 'family') and fine-grained attribute-level controls (e.g., 'phone number', 'email'). This segmentation allows users to set broad preferences easily while automatically applying precise attribute-level protections, resolving the contradiction between ease of operation and privacy protection accuracy.
Solution Approach 2:
The system applies different levels of privacy control to different attributes based on their sensitivity. Highly sensitive attributes (phone number, email) receive automatic fine-grained protection regardless of coarse preferences, while less sensitive attributes follow user preferences. This local quality approach ensures critical information is always protected while maintaining ease of use for general settings.
2Manufacturing precision
If multiple levels of security and complex privacy settings schemes are implemented, then manufacturing precision (privacy protection accuracy) is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary analysis of user attributes, relationships, and potential risks before privacy violations occur. By pre-calculating privacy indices and identifying sensitive attributes, the system automatically applies appropriate protection levels without requiring users to navigate complex multi-level security configurations, thus maintaining high privacy protection accuracy while reducing system complexity.
Solution Approach 2:
The privacy management system operates autonomously by automatically analyzing user data, calculating privacy indices, and applying appropriate privacy settings based on calculated indices and predefined policies. This self-service mechanism eliminates the need for users to manually configure complex multi-level security settings while maintaining high privacy protection accuracy.
3Manufacturing precision
If users are required to manually configure detailed privacy settings, then manufacturing precision (privacy protection accuracy) is improved, but ease of operation deteriorates
Solution Approach 1:
The system introduces an intermediary privacy management layer that sits between user preferences and actual data exposure. This intermediary automatically translates coarse user preferences into fine-grained attribute-level privacy settings, eliminating the need for users to manually configure detailed settings while ensuring accurate privacy protection through automated attribute-level control.
Solution Approach 2:
The privacy system performs self-service by automatically analyzing user attributes, relationships, and sensitivity levels to generate and apply appropriate privacy settings. Users simply express their general privacy preferences, and the system autonomously configures detailed attribute-level protections, combining ease of operation with high privacy protection accuracy.
Data Source
AI summary
Methods for managing privacy settings for a social network using an electronic computing device are presented including: causing the electronic computing device to receive a triggering event on the social network; and causing the electronic computing device to determine a number of privacy indices in response to the triggering event, where the number of privacy indices correspond with at least one target user, where the number of privacy indices are normalized from a summation of a number of privacy scores, where the number of privacy scores are each derived from a sum of attribute scores, and where the sum of attribute scores are each derived from a weighted sensitivity value of an attribute and a user relationship distance value of a user and the target user.