EEG-Based Encryption Key Generation Using Neural Networks
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Solution Overview
Problem
Current cryptographic systems, particularly public key systems, face challenges in maintaining security due to the potential derivation of private decryption keys from publicly available encryption keys, which can lead to unauthorized access and message forgery.
Innovation Solution
A method and system that utilize artificial neural network (ANN) models to map and transform electroencephalogram (EEG) data signals from user-specific neural activities into unique encryption keys and signatures, ensuring that only authorized users can decrypt messages and verify signatures based on their individual brain activity patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If public key encryption is used to enable secure communication without pre-shared secrets, then ease of operation is improved, but security is worsened due to the possibility of deriving private keys from public keys
Solution Approach 1:
The patent introduces an intermediary biometric authentication system between the public key and private key. Instead of directly deriving the private key from the public key, a biometric scanner verifies the user's physical characteristics (fingerprint, iris, facial recognition) as an intermediary step. This mediator ensures that even if someone obtains the public key, they cannot derive the private key without the user's biometric data, thus resolving the security vulnerability while maintaining ease of operation.
2Reliability
If symmetric key encryption is used to ensure strong security, then security is improved, but ease of operation is worsened due to the requirement of secure key exchange
Solution Approach 1:
The patent implements a self-service biometric authentication system where the user's own biometric data serves as the key exchange mechanism. The biometric scanner captures and verifies the user's unique physical characteristics locally, eliminating the need for secure manual key exchange between parties. The system automatically generates and manages cryptographic keys based on the verified biometric data, making strong security accessible without requiring secure key distribution infrastructure.
Solution Approach 2:
The patent replaces the mechanical process of secure key exchange (physical meeting, secure channel transmission) with a biometric authentication system. Instead of manually exchanging symmetric keys through secure channels, the system uses biometric scanners to capture and verify user characteristics, automatically generating cryptographic keys. This substitution eliminates the complexity of secure key distribution while maintaining strong security through the uniqueness of biometric data.
3Reliability
If traditional cryptographic systems are used to prevent unauthorized access, then security is improved, but adaptability is worsened due to the static nature of encryption keys
Solution Approach 1:
The patent introduces dynamic adaptability to cryptographic systems by integrating biometric authentication. Instead of using static encryption keys that remain unchanged, the system dynamically generates and updates cryptographic keys based on the user's biometric data. The biometric scanner continuously verifies user characteristics, and the system adapts the cryptographic parameters accordingly. This dynamic approach maintains strong security while enabling the system to adapt to different users and contexts, resolving the contradiction between security and adaptability.
Data Source
AI summary
A user-specific, electroencephalogram data-based secure encryption generator maps artificial neural network neuron elements to electroencephalogram data signals generated from scanning neural activity of a user while the user executes a mental activity. Weighting factors are trained to transform the electroencephalogram data signals into a first set of weighted signals that are different from weighted signals generated from scanning neural activity of the user while the user executes another, different activity, and from weighted signals generated from scanning neural activity of another user while executing a similar mental activity. The trained weighting factors are associated with the first set of electroencephalogram data signals and the current mental activity. Thus, a reproducible electroencephalogram encryption key is defined that is unique to the user as a function of one or both of the trained weighting factors and the first weighted set of electroencephalogram data signals.