Trusted Execution Environment for Secure Machine Learning Model Generation
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Solution Overview
Problem
Machine learning models trained on sensitive personal data, such as personally identifiable information (PII), face risks of data mishandling, leading to privacy and economic consequences, as existing methods fail to securely manage and distribute these models without exposing the underlying data.
Innovation Solution
A federated machine learning training process that utilizes secure hardware components like trusted execution environments (TEEs) to securely generate, store, and distribute anonymized models across decentralized edge nodes, ensuring that PII is not shared or exposed, and employs distributed verification and approval mechanisms to ensure the model's integrity and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained on sensitive personal data (PII), then the model's predictive accuracy and utility are improved, but the risk of PII exposure and privacy breaches increases
Solution Approach 1:
The system segments the model distribution process into multiple isolated stages: training on encrypted data, generating encrypted model weights, verifying model integrity through approval nodes, and controlled deployment. Each stage operates with restricted access to PII, eliminating the need to share sensitive data while maintaining model accuracy.
Solution Approach 2:
Homomorphic encryption serves as an intermediary that enables mathematical operations on encrypted data without decryption. This allows the model to be trained on PII while the data remains encrypted, preventing exposure during the training process while still achieving accurate results.
2Productivity
If machine learning models are distributed across decentralized networks, then the system's scalability and accessibility are improved, but the security and control over sensitive data deteriorate
Solution Approach 1:
The system performs preliminary verification and approval actions before model distribution. Approval nodes verify model integrity and authorize deployment in advance, ensuring that only verified models are distributed across the network. This preliminary control mechanism maintains security while enabling efficient decentralized distribution.
Solution Approach 2:
The model distribution system dynamically adapts to decentralized network conditions while maintaining security protocols. The system can selectively distribute models to authorized nodes based on verification results, enabling flexible and scalable deployment without compromising data security through rigid centralized control.
3Reliability
If sensitive data is shared across multiple nodes for model training, then the model's robustness and generalization are improved, but the vulnerability to data breaches and misuse increases
Solution Approach 1:
The system changes the cryptographic parameters of the data throughout the training process. PII is encrypted using homomorphic encryption, allowing multiple nodes to contribute to model training with encrypted data from different sources. This parameter transformation enables robust model training through diverse data inputs while maintaining encryption throughout, preventing data breaches even as data is shared across nodes.
Data Source
AI summary
Systems, apparatuses, and methods include technology that stores, with a server, a plurality of machine learning models in a first trusted execution environment. The plurality of machine learning models is associated with personal identifiable information. The technology generates, in the first trusted execution environment, a derivative machine learning model based on the plurality of machine learning models, and determines, with the server, that the derivative machine learning model will be transmitted to an approval node over a secure channel.