Adaptive Authentication System for Blockchain Key Storage
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Solution Overview
Problem
Current authentication methods face challenges in providing non-intrusive, privacy-preserving, and adaptive authentication solutions that effectively manage risk based on user environment and location, while ensuring strong security and usability, especially in scenarios where explicit user interaction is not feasible and location data privacy is a concern.
Innovation Solution
The implementation of a non-intrusive privacy-preserving authentication system that uses a combination of explicit and non-intrusive authentication techniques, including location-based and behavioral data, to dynamically adjust authentication levels based on user behavior and environmental factors, ensuring privacy protection and adaptive authentication policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If explicit user interaction authentication is used, then authentication security is improved, but user friction and complexity increase
Solution Approach 1:
The system dynamically adjusts authentication requirements based on risk assessment of the current environment and user behavior patterns. When risk is low and user behavior is consistent with enrolled patterns, the system allows silent authentication without explicit user interaction. When risk increases or behavior deviates, explicit authentication is triggered, creating a dynamic balance between security and usability.
Solution Approach 2:
The system uses machine learning models that continuously learn from user behavior patterns to automatically assess authentication risk and make authentication decisions without requiring explicit user input in low-risk scenarios. The enrolled user behavior patterns serve as the foundation for automatic authentication, reducing friction while maintaining security.
2Measurement precision
If location data is collected for authentication, then authentication accuracy is improved, but user privacy is compromised
Solution Approach 1:
The system extracts only the necessary location information (distance from enrolled location) required for risk assessment while not storing or transmitting the user's actual location data. The authentication decision is based on the extracted distance metric rather than the raw location information, preserving privacy while maintaining authentication accuracy.
Solution Approach 2:
The system uses an intermediary approach where location data is processed locally on the device to determine distance from enrolled locations, and only the authentication decision (not the location data itself) is transmitted to remote servers. This intermediary processing layer protects user privacy while enabling accurate location-based authentication.
3Ease of operation
If silent authentication without user interaction is used, then user friction is reduced, but authentication certainty decreases
Solution Approach 1:
The system continuously monitors user behavior patterns and provides feedback to the authentication decision-making process. Machine learning models analyze real-time behavior data against enrolled patterns to dynamically adjust authentication certainty. When behavior patterns match expectations, silent authentication is confirmed; when deviations occur, the system triggers explicit authentication to restore certainty.
Solution Approach 2:
The authentication system dynamically adjusts the level of certainty required based on the assessed risk of the current environment and the match quality between observed and enrolled user patterns. In low-risk scenarios with high pattern match quality, lower certainty thresholds are accepted for silent authentication. In high-risk scenarios or when pattern matching is poor, higher certainty is required, triggering explicit authentication.
4Reliability
If authentication requirements are increased for high-value transactions, then security is improved, but productivity decreases
Solution Approach 1:
The system dynamically adjusts authentication requirements based on the assessed risk of each transaction, considering factors such as transaction value, user behavior patterns, and environmental risk indicators. For low-risk transactions, minimal or silent authentication is required, enabling fast processing. For high-risk transactions, enhanced authentication is triggered, balancing security needs with transaction throughput.
Data Source
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AI summary
A system, apparatus, method, and machine readable medium are described for secure authentication. For example, one embodiment of a system comprises: an authenticator on a client device to securely store one or more private keys, at least one of the private keys usable to authenticate a block of a blockchain; and an attestation module of the authenticator or coupled to the authenticator, the attestation module to generate a signature using the block and the private key, the signature usable to attest to the authenticity of the block by a device having a public key corresponding to the private key.