Neural Network Integrity Assurance via Transient-Key Cryptography
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Solution Overview
Problem
Neural networks are susceptible to adversarial attacks and tampering, which can compromise their integrity and authenticity, and existing security measures do not adequately address these threats.
Innovation Solution
The use of transient-key cryptography to certify and validate the internal state of neural network models by associating them with specific time intervals, ensuring that any unauthorized changes can be detected and verified.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural networks are deployed for data-driven applications, then their capability to decipher intricate correlations in expansive datasets is improved, but their susceptibility to adversarial attacks and tampering increases
Solution Approach 1:
The system performs preliminary cryptographic hashing of tensor data structures before they are deployed or modified. By pre-computing and storing hash values of the original tensor states, the system establishes a baseline for future integrity verification, preventing undetected tampering while maintaining the neural network's analytical capabilities
Solution Approach 2:
The patent introduces cryptographic hash functions and timestamping authorities as intermediary components between the neural network and its environment. These intermediaries verify tensor integrity without interfering with the network's core function of deciphering data correlations, thus maintaining both versatility and reliability
2Reliability
If cryptographic techniques are applied to protect tensor data structures, then security against adversarial attacks is improved, but computational overhead and system complexity increase
Solution Approach 1:
The patent extracts the cryptographic verification function into a separate, dedicated system that operates independently from the neural network's core computation. By isolating the hashing and verification operations from the main inference pipeline, the system adds security without significantly increasing the complexity of the neural network's primary function
Solution Approach 2:
The system changes the parameter of tensor representation by adding cryptographic hash values alongside the original tensor data. This parameter change enables security verification while maintaining the original tensor's functional properties, as the hash serves as an additional verification layer rather than modifying the tensor's computational role
3Measurement precision
If timestamping is used to verify the state of tensor data structures, then detectability of unauthorized changes is improved, but processing time and loss of time increase
Solution Approach 1:
The system pre-computes and stores hash values of tensor data structures at specific time intervals or before deployment. By establishing these temporal baselines in advance, the system enables rapid verification of unauthorized changes without requiring time-consuming analysis during critical verification moments
Solution Approach 2:
The patent creates cryptographic copies (hashes) of the tensor data structures that can be verified without accessing or processing the original large-scale tensor data. These compact hash copies enable rapid detection of changes while minimizing the time required for verification compared to analyzing the full tensor structures
Data Source
AI summary
The present invention provides a computer system and method for ensuring the architectural integrity and authenticity of neural networks using cryptographic techniques and timestamping mechanisms. The system certifies and validates the internal state of tensor and graph data structures within neural networks by computing hash values of the network's tensors and graphs and associating these values with specific time intervals. This can be achieved through use of transient-key cryptography, wherein a time-specific cryptographic key pair is generated for each interval, allowing the signing of hash values and ensuring data integrity. A timestamping authority issues timestamp tokens for the hash values, which are stored in a distributed, redundant archive for future verification. The validation process involves comparing the current state of the neural network with the stored hash values, ensuring any tampering or unauthorized modifications can be detected. This approach enhances the security and trustworthiness of neural network models across critical applications by safeguarding against adversarial attacks and model poisoning.