Group Trust Score Calculation for Social Robot Privacy
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Solution Overview
Problem
Social robots struggle to understand relationships among multiple users, leading to general reactions that do not consider the context of trust within a group.
Innovation Solution
An information processing device that calculates a group trust score based on different trust levels (social, familial, professional) and adjusts user information handling levels accordingly, recognizing roles and environmental factors to determine trust scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If privacy protection techniques are applied to handle user information, then user privacy is protected, but the system cannot understand relationships among multiple users
Solution Approach 1:
The patent segments user information into two distinct categories: privacy information (which requires protection through anonymization and pseudonymization) and relationship information (which requires preservation for understanding user dynamics). By applying different processing rules to different information types, the system can protect privacy while maintaining relationship context.
Solution Approach 2:
The patent applies local quality by implementing context-dependent privacy handling. Different types of user information receive different levels of privacy protection based on their nature and sensitivity. Relationship information receives preserved treatment while sensitive personal information receives anonymized treatment, allowing the system to maintain necessary context while protecting privacy.
2Ease of operation
If general user information handling is used, then processing is simple, but the robot reacts generally without considering trust context
Solution Approach 1:
The patent implements dynamic user information handling levels that adjust based on the calculated group trust score. The system transitions from static, one-size-fits-all privacy handling to dynamic, context-aware handling where the degree of anonymization and the level of information access change according to the trust relationship detected in the current interaction context.
Solution Approach 2:
The patent changes the parameter of information handling granularity based on trust levels. When trust is high, the system uses finer-grained information access allowing detailed personal information to be utilized. When trust is low or uncertain, the system coarsens the information handling by applying stronger anonymization, thus adapting processing complexity to match the trust context.
3Loss of information
If detailed user information is processed, then understanding of user relationships improves, but privacy protection becomes more difficult
Solution Approach 1:
The patent introduces trust score calculation as an intermediary mechanism that mediates between detailed information processing and privacy protection. The trust score, derived from analyzing interaction patterns and user behavior, serves as a gatekeeper that determines the appropriate level of information detail to process. This intermediary allows the system to dynamically balance information utilization with privacy preservation based on established trust relationships.
Data Source
AI summary
An information processing device includes a score calculation unit configured to calculate a group trust score indicating a relationship among a plurality of users in a group in which at least two users are present from a plurality of different trust levels and change a user information handling level on the basis of the group trust score.