Personalized Avatar Anonymization for Affinity-Based Privacy
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Solution Overview
Problem
Existing virtual environments lack adequate mechanisms to customize avatars with both static and dynamic characteristics to provide varying levels of privacy and security based on user affinity, leading to potential identification of users through their avatar's appearance and motion.
Innovation Solution
Implementing a system that uses machine learning models, such as generative adversarial networks (GANs) for static anonymization and time-series data processing for dynamic anonymization, to customize avatars based on user affinity groups, obfuscating or preserving identity depending on the user's relationship with the operator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If avatar characteristics are made highly detailed and realistic, then user immersion and entertainment value improve, but user identification and privacy security deteriorate
Solution Approach 1:
The patent applies local quality by implementing different privacy protection levels for different characteristics and affinity groups. Specifically, the system selectively anonymizes certain avatar characteristics (such as facial features, body shape, or motion patterns) based on the viewer's affinity group, while preserving other characteristics. This allows the avatar to maintain high detail and realism for immersion purposes while simultaneously protecting user identity from unauthorized identification, thus resolving the contradiction between customization quality and privacy security.
Solution Approach 2:
The patent implements dynamics by making avatar characteristics dynamically adjustable based on the viewer's affinity group classification. The system continuously evaluates the relationship between users and dynamically modifies avatar presentation in real-time. For example, close friends may see highly detailed and recognizable avatars, while strangers see more anonymized versions. This dynamic adaptation allows the system to optimize both immersion and security contextually, resolving the static contradiction between detail and privacy.
2Measurement precision
If comprehensive user data is collected for accurate affinity classification, then privacy policy customization improves, but data security and privacy management complexity increase
Solution Approach 1:
The patent applies segmentation by dividing the affinity group classification system into distinct, manageable segments or categories (e.g., close friends, acquaintances, strangers, colleagues). Each segment has predefined privacy policies and anonymization levels, which simplifies the management of complex privacy requirements. Instead of handling continuous spectrum of relationships, the system segments them into discrete groups, making the overall system more tractable while maintaining sufficient classification accuracy for effective privacy protection.
Solution Approach 2:
The patent implements preliminary action by pre-configuring privacy policies and anonymization parameters for each affinity group segment before actual user interactions occur. The system establishes default privacy rules, characteristic selection criteria, and anonymization levels in advance for each classification category. This preliminary setup reduces the computational and management complexity during runtime, as the system only needs to classify users into predefined segments and apply corresponding pre-configured policies, rather than making complex decisions in real-time.
Data Source
AI summary
Mechanisms are provided for customizing an avatar in a virtual environment. A user that is interacting with the virtual environment, and to whom the avatar is to be rendered in the virtual environment, is identified and classified with regard to a plurality of affinity groups specifying levels of affinity between the first user and an operator of the avatar. A user security and privacy policy (USPP) data structure, associated with the operator, is retrieved that specifying security and privacy policies for each affinity group in the plurality of affinity groups. Based on the user classification and the security and privacy policy, an anonymization operation is applied to static and/or dynamic characteristics data for the operator which are used to render the avatar, to thereby generate anonymized characteristic data. The anonymized characteristic data is output for rendering the avatar in the virtual environment.