Homomorphic Encryption for Secure Machine Learning Training
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Solution Overview
Problem
Deep learning networks face challenges in training with sensitive data while maintaining confidentiality and privacy, as revealing training data can violate regulations and incur liabilities, necessitating a secure and private method for training machine learning models.
Innovation Solution
A cloud-based security service that validates and preprocesses training data using homomorphic encryption and random noise generation, enabling secure and private training of machine learning models by generating encrypted training data and applying adversarial training techniques to protect against data poisoning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training data is revealed for model training, then machine learning model can be trained effectively, but data confidentiality and privacy are violated
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary mechanism that allows the training data to remain encrypted during the training process. The encryption scheme enables mathematical operations on ciphertexts without decryption, serving as a mediator between the need for data access (for training) and data protection (confidentiality requirements). This resolves the contradiction by allowing effective model training while maintaining data privacy through the intermediary encryption layer.
2Reliability
If data validation is performed using ordinary differential equations, then data integrity is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent applies ordinary differential equations to validate training data before the actual machine learning training process begins. This preliminary validation ensures data integrity and detects potential poisoning attacks in advance, preventing wasted computational resources during training. By performing validation beforehand, the system trades some initial processing time for significant time savings and reliability improvements during the main training operation.
3Object-affected harmful factors
If homomorphic encryption is applied to training data, then data security is enhanced, but computational overhead and processing speed decrease
Solution Approach 1:
The patent creates encrypted copies of the training data using homomorphic encryption, allowing the model training to proceed on these encrypted copies rather than requiring access to plaintext data. This copying approach enables data security to be maintained while the training process operates on the encrypted representations, effectively decoupling the security requirements from the processing speed constraints of the original data.
4Reliability
If adversarial training with random noise is applied, then model robustness against data poisoning is improved, but training complexity and resource requirements increase
Solution Approach 1:
The patent implements adversarial training by introducing random noise and adversarial examples into the training data before the main training process. This preliminary anti-action prepares the model against potential poisoning attacks by exposing it to corrupted data variations in advance. The technique enhances model robustness by pre-acclimating the model to adversarial conditions, reducing its vulnerability to actual poisoning attacks during deployment.
Data Source
AI summary
Embodiments are disclosed for a method. The method includes validating training data that is provided for training a machine learning model using ordinary differential equations. The method further includes generating pre-processed training data from the validated training data by generating encrypted training data from the validated training data using homomorphic encryption and generating random noise based on the validated training data. The method also includes training the machine learning model adversarially with the pre-processed training data.