Device Identification via Posterior Probability and Historical Data
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Solution Overview
Problem
Existing systems face challenges in reliably identifying and authenticating computer devices to prevent unauthorized access to protected content, especially due to the phasing out of third-party cookies and the inconvenience of one-time passwords, as well as the difficulty in maintaining effective blacklists across different system domains.
Innovation Solution
A method and system that utilize a computer device identification node to generate posterior probability values based on device signature values and historical data, determining the likelihood of a suspect device being genuine, and controlling access accordingly, employing a Naïve Bayes classifier and updating mean and standard deviation values in a historical repository.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If third-party cookies are used to identify computer devices, then device identification is simplified, but privacy concerns increase and user control over identification decreases
Solution Approach 1:
The patent extracts the device identification function from third-party cookies and implements it through first-party cookies and local storage mechanisms controlled by the user's own domain. This removes the harmful third-party tracking aspect while preserving the beneficial device identification capability.
Solution Approach 2:
The patent introduces a domain-controlled identification system as an intermediary between the user's device and external services. This intermediary maintains device identification capabilities while enforcing privacy controls and user consent requirements, mediating between identification needs and privacy protection.
2Reliability
If one-time passwords are used for user authentication, then security against unauthorized access is improved, but user convenience deteriorates
Solution Approach 1:
The patent performs preliminary device identification and trust establishment before authentication is needed. By pre-establishing device trust through domain-controlled identification and maintaining device profiles with historical authentication data, the system reduces the need for frequent one-time passwords while maintaining security.
Solution Approach 2:
The patent implements dynamic authentication requirements based on device trust levels and contextual factors. Rather than uniformly requiring one-time passwords for all access attempts, the system adapts authentication strength based on device reputation, historical behavior, and risk assessment, making frequent access more convenient while maintaining security for suspicious activities.
3Reliability
If blacklists are maintained by individual computer systems to prevent unauthorized access, then local security is improved, but effectiveness across different system domains deteriorates
Solution Approach 1:
The patent creates a universal device identification system that works across multiple domains and systems. By using domain-controlled first-party cookies and standardized device profiles, the same identification mechanism serves multiple purposes: local access control, cross-domain recognition, fraud detection, and device reputation tracking, replacing fragmented blacklists with a unified system.
Solution Approach 2:
The patent merges individual system blacklists into a centralized device reputation system that aggregates security data across domains. Instead of separate blacklists maintained by each system, the patent combines them into a unified device profile that tracks device behavior and reputation across multiple contexts, improving cross-domain effectiveness while maintaining local enforcement capabilities.
4Reliability
If device identification systems are made more sophisticated to detect impersonation, then security against fraud is improved, but system complexity increases
Solution Approach 1:
The patent creates simplified copies of device characteristics through standardized identification tokens and profiles rather than analyzing complex device hardware details. By copying essential device attributes into standardized first-party cookies and local storage formats, the system achieves fraud detection capability without requiring complex analysis infrastructure.
Solution Approach 2:
The patent changes the parameters of device identification from complex hardware fingerprinting to simpler, domain-controlled cookie-based identification. By transforming the identification space from detailed hardware characteristics to standardized domain-authenticated tokens, the system reduces complexity while maintaining fraud detection effectiveness through cryptographic verification and reputation tracking.
Data Source
AI summary
A historical repository of device identifiers associated with sets of prior mean values and prior standard deviation values, is maintained. A device identification message is received from a suspect computer device and contains a device identifier and a measured device signature value. A mean value and a standard deviation value are determined based thereon, and used to update the prior mean value and the prior standard deviation value in one of the sets in the historical repository having an association to the device identifier. A prior frequency value is determined. A posterior probability value, indicating likelihood that the suspect computer device corresponds to a genuine computer device identified by the device identifier, is generated based on the mean value, the standard deviation value, and the prior frequency value. Whether the suspect computer device is permitted to access an electronic resource is controlled based on the posterior probability value.