Network Identity Disclosure Feedback for Fraud Detection
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Solution Overview
Problem
In computer networks where users can maintain multiple identities, accurately quantifying the number of true users and valuing network contributions becomes challenging, leading to overstated network value and fraudulent activities.
Innovation Solution
Implementing techniques for establishing collective identity confidence through mutual self-disclosure processes, incentivizing users to maintain one real identity, and using a consensus network to verify and store identity information, which helps in identifying fraudulent actors and enhancing network value by encouraging genuine user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users are allowed to maintain multiple identities on the network, then user engagement and network activity appear to increase, but the accuracy of network value assessment deteriorates due to overstated user counts and fraudulent activities
Solution Approach 1:
The system implements a feedback mechanism where users receive notifications when their identity behavior is suspicious or fraudulent, and their user status is dynamically adjusted based on their identity disclosure activities. This feedback loop encourages genuine users to maintain consistent identities while penalizing fraudulent actors, thereby improving the accuracy of network value assessment without suppressing legitimate network activity.
Solution Approach 2:
The patent introduces an intermediary verification mechanism where a computing system acts as a mediator between users and network value assessment. This intermediary analyzes identity disclosure activities, cross-references user behaviors, and determines authentic user status, separating genuine user activity from fraudulent attempts while maintaining overall network productivity.
2Measurement precision
If identity verification processes are implemented to detect fraudulent users, then measurement precision of user authenticity improves, but device complexity and operational difficulty increase
Solution Approach 1:
The system employs self-service mechanisms where users automatically disclose their identities through their network activities and interactions. The computing system passively monitors and analyzes these disclosed identities rather than requiring active verification from users, reducing operational complexity while maintaining high measurement precision in detecting fraudulent users.
Solution Approach 2:
The patent utilizes copying of user behavior patterns and identity characteristics across different network interactions. By analyzing consistent copies of user behavior and identity disclosure across multiple transactions, the system can detect anomalies and fraudulent activities without requiring complex verification infrastructure, thereby improving authenticity detection while managing system complexity.
3Reliability
If incentives are provided to encourage single identity maintenance, then user authenticity and network health improve, but loss of network value occurs through reduced flexibility in user participation
Solution Approach 1:
The system implements dynamic user status adjustment rather than static restrictions. Users can maintain multiple identities temporarily for legitimate purposes (such as testing or different use cases), but the system dynamically monitors and adjusts their status based on their actual behavior patterns. This dynamic approach maintains network health and reliability while preserving necessary adaptability and user participation flexibility.
Solution Approach 2:
The patent changes the parameters of user participation by introducing user status levels and corresponding privilege structures. Instead of rigidly preventing multiple identities, the system adjusts participation parameters based on verified authenticity, allowing genuine users to participate flexibly while restricting fraudulent actors. This parameter-based approach maintains both network health and user versatility.
Data Source
AI summary
Techniques described herein include techniques for communicating information about the social behavior of users and transactions performed by users on a computer network. In one example, this disclosure describes a method that includes receiving information about identity disclosure activities performed by each of a plurality of users on the network; determining that the information about identity disclosure activities includes information consistent with a prior identity disclosure activity performed by a first user having a first user status on the network; increasing the first user status; determining that the information about identity disclosure activities includes information that is not consistent with a prior identity disclosure activity performed by a second user having a second user status on the network; and decreasing the second user status.