External Account Authentication Using Cross-Account Activity Matching
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Solution Overview
Problem
Existing systems fail to leverage data exchange between segregated user accounts for enhanced security measures, preventing the integration of security benefits across multiple accounts.
Innovation Solution
A system and method for authenticating external accounts using a machine learning model that analyzes the overlap or correlation between user activity data from a secure account and external account content, generating a certainty level for pairing the accounts based on a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users operate multiple accounts separately, then account security is maintained through isolation, but security measures cannot be exchanged or shared between accounts
Solution Approach 1:
The patent combines data from multiple segregated accounts (secure account and external account) into a unified authentication process. The machine learning model receives user activity data from the secure account and external data from the external account, merging these previously isolated data sources to generate a certainty level for authentication, thereby enabling security measure exchange while maintaining account isolation
2Device complexity
If traditional authentication systems are used without data exchange, then system simplicity is maintained, but processing time and resources increase for security verifications
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing user activity data from the secure account and external data from the external account before authentication is needed. When authentication is required, the machine learning model can quickly process this pre-prepared data to generate a certainty level, significantly reducing processing time compared to traditional real-time verification methods
3Measurement precision
If external account content is analyzed using machine learning, then authentication accuracy is improved, but computational resources and processing complexity increase
Solution Approach 1:
The machine learning model applies local quality by focusing on specific relevant features and patterns within the user activity data and external data rather than processing all data uniformly. The model identifies and weights particular data characteristics that are most indicative of authentication certainty, thereby improving accuracy while reducing the overall computational burden by concentrating resources on the most discriminative features
Data Source
AI summary
Systems and methods for external account authentication are disclosed herein. They include receiving a call to pair the external account with a secure account, extracting external data from the external account, the external data corresponding to external account content, providing user activity data from the secure account as an input to an authentication machine learning model, providing the external data as an input to the authentication machine learning model, the authentication machine learning model configured to output a certainty level that the external account is associated with a user of the secure account based on the external data and the activity data, receiving the certainty level from the authentication machine learning model, determining that the certainty level meets a certainty threshold, and pairing the external account with the secure account based on determining that the certainty level meets the certainty threshold.