Privacy Score Calculation for Social Network Profile Items
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Solution Overview
Problem
Existing social networking platforms inadequately address individual users' privacy concerns, as current privacy settings are often poorly managed due to one-dimensional sensitivity evaluations that fail to account for varying social group contexts, leading to increased risks of identity theft and digital stalking.
Innovation Solution
A method and system for calculating a privacy score for users in social networks by assessing the sensitivity and visibility of shared profile items based on relationships with other users, allowing users to adjust settings automatically along a continuum, incorporating multiple relationship types and their numeric levels to provide a comprehensive privacy management framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If default privacy settings are used to simplify user operation, then ease of operation is improved, but user privacy protection deteriorates
Solution Approach 1:
The system automatically calculates privacy scores and adjusts privacy settings without requiring user intervention. The privacy management system serves itself by continuously monitoring shared information and autonomously optimizing privacy protection based on calculated scores, eliminating the need for users to manually configure complex privacy settings while maintaining strong privacy protection
Solution Approach 2:
The system implements a feedback mechanism where privacy scores are continuously calculated based on shared information and relationship data, then this feedback is used to automatically adjust privacy settings. The system monitors the effectiveness of privacy settings and refines them over time based on the calculated privacy scores, creating a closed-loop system that improves privacy protection dynamically
2Device complexity
If one-dimensional sensitivity evaluation is used to simplify privacy assessment, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The system transitions from one-dimensional sensitivity evaluation to multi-dimensional privacy assessment by incorporating relationship types, sharing contexts, and social group memberships as additional dimensions. The privacy score calculation considers multiple factors including the type of relationship between users, the context in which information is shared, and the sensitivity of different data elements, creating a comprehensive multi-dimensional evaluation framework that accurately reflects privacy value in various social contexts
Solution Approach 2:
The privacy assessment system segments the evaluation into distinct components: relationship type analysis, sharing context evaluation, data sensitivity assessment, and privacy score calculation. Each component is evaluated separately and then integrated to produce the final privacy score, allowing the system to manage complexity through modular segmentation while achieving precise multi-dimensional measurement
3Object-affected harmful factors
If comprehensive privacy settings are provided to improve privacy protection, then privacy protection is improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically calculates privacy scores and adjusts privacy settings without requiring user intervention. The privacy management system serves itself by continuously monitoring shared information and autonomously optimizing privacy protection based on calculated scores, eliminating the need for users to manually configure complex privacy settings while maintaining strong privacy protection
Solution Approach 2:
The system dynamically changes privacy parameters based on calculated privacy scores. Instead of presenting users with static complex settings, the system automatically adjusts privacy parameters such as visibility levels and sharing restrictions based on the computed privacy scores, transforming the complex multi-dimensional privacy management into automatic parameter adjustments that simplify user interaction while maintaining comprehensive protection
Data Source
AI summary
Methods for providing a privacy setting for a target user relative to relationships with a number of other users in a social network utilizing an electronic computing device are presented, the method including: causing the electronic computing device to retrieve a current privacy setting for a common profile item, where the common profile item corresponds with the target user and each of the number of other users, and where the common profile item is one of a number of common profile items; causing the electronic computing device to calculate a pseudo-common profile item sensitivity value for the common profile item based on the current privacy settings of the target user and the number of other users; causing the electronic computing device to calculate a final common profile item sensitivity value for the common profile item based on the current privacy setting.