Encrypted Biometric Identity Vectors for Secure Cross-Device Authentication
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Solution Overview
Problem
Current identification and authentication methods in computing environments are insecure, with user identifiers and passwords being inadequate, and existing augmentations such as multifactor authentication failing to fully address security concerns.
Innovation Solution
A private identity system using fully encrypted biometric and behavioral information to securely identify users, enabling seamless functionality across devices and switching between users with minimal overhead, employing pre-trained embedding networks and classification networks to generate and recognize encrypted feature vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional user identifiers and passwords are used for authentication, then the system is easy to operate, but the security is insufficient
Solution Approach 1:
The patent replaces traditional mechanical authentication systems (usernames and passwords) with a biometric-based authentication system using neural networks. The embedding network processes biometric data to generate encrypted feature vectors, which are then authenticated by classification networks, substituting the manual password entry mechanism with automated biometric verification.
Solution Approach 2:
The patent introduces encrypted feature vectors as an intermediary between the biometric input and the authentication decision. These feature vectors serve as a secure representation that preserves privacy while enabling authentication, acting as a mediator that transforms raw biometric data into a form suitable for secure comparison and verification.
2Reliability
If multifactor authentication is implemented to augment security, then the security profile improves, but the system complexity increases
Solution Approach 1:
The patent merges multiple authentication factors into a unified neural network-based system. Instead of implementing separate multifactor authentication mechanisms (combining password, SMS code, and security questions), the system integrates biometric verification, behavioral analysis, and device characteristics into a single cohesive authentication framework processed through embedding and classification networks.
Solution Approach 2:
The patent changes the fundamental parameters of authentication by transitioning from discrete authentication factors (password strings, numeric codes) to continuous biometric feature spaces. The system uses encrypted feature vectors that capture nuanced biometric and behavioral parameters, enabling more sophisticated security verification without requiring multiple separate authentication steps.
3Reliability
If encrypted biometric information is used for identification, then the security is enhanced, but the processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the embedding network with large datasets before deployment. The neural network is pre-trained to recognize and encode various biometric features into encrypted feature vectors, so that during actual authentication, the system can quickly process and compare features without requiring complex real-time analysis, reducing processing complexity while maintaining security.
Solution Approach 2:
The patent creates encrypted copies of biometric data in the form of feature vectors. Instead of storing or processing raw biometric information, the system generates encrypted feature vector representations that preserve the essential characteristics needed for authentication while protecting the original biometric data, simplifying processing while enhancing security.
4Ease of operation
If seamless user switching across devices is enabled, then the user experience improves, but the risk of unauthorized access increases
Solution Approach 1:
The patent adds another dimension to authentication by incorporating device-specific characteristics and contextual information alongside biometric data. The embedding network processes not only user biometrics but also device identifiers, location data, and usage patterns, creating multi-dimensional encrypted feature vectors that enable seamless user switching while maintaining security through contextual verification.
Solution Approach 2:
The patent implements feedback mechanisms where the classification networks continuously verify authentication decisions and adjust their thresholds based on observed patterns. The system monitors authentication attempts across devices and uses feedback from successful and failed verifications to dynamically adjust security parameters, enabling smooth user transitions while detecting and preventing unauthorized access attempts.
Data Source
AI summary
In various embodiments, a fully encrypted private identity based on biometric and/or behavior information can be used to securely identify any user efficiently. According to various aspects, once identification is secure and computationally efficient, the secure identity/identifier can be used across any number of devices to identify a user an enable functionality on any device based on the underlying identity, and even switch between identified users seamlessly all with little overhead. In some embodiments, devices can be configured to operate with function sets that transition seamlessly between the identified users, even, for example, as they pass a single mobile device back and forth. According to some embodiments, identification can extend beyond the current user of any device, into identification of actors responsible for activity/content on the device.