Machine Learning Training in Sequestered Enclaves for Financial Data
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Solution Overview
Problem
The complexity of analyzing diverse financial data types and sources poses challenges in effective data analysis, exacerbated by the need to handle sensitive information securely.
Innovation Solution
A machine learning model is trained on a first corpus and instantiated in a sequestered enclave, using a second corpus of sequestered information to generate outputs while ensuring data security and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained on diverse financial data from multiple sources, then analysis capability and model accuracy are improved, but data security and privacy protection deteriorate
Solution Approach 1:
The system segments the machine learning model training process into two distinct phases: initial training on public financial data outside the enclave, and subsequent fine-tuning on sensitive sequestered data within the secure enclave. This segmentation allows the model to benefit from both public data volume and sensitive data quality while maintaining security boundaries.
Solution Approach 2:
The sequestered enclave acts as an intermediary secure environment that mediates between the need to access sensitive financial data for model improvement and the requirement to protect that data's confidentiality. The enclave enables controlled interaction between the ML model and sensitive data without compromising security.
2Productivity
If sensitive financial data is accessed for model retraining, then model performance on specific financial tasks is improved, but exposure to security risks increases
Solution Approach 1:
The sequestered enclave creates an inert secure environment isolated from external network threats and unauthorized access. Sensitive financial data remains in this protected environment during model retraining, preventing exposure to external security risks while still enabling model performance improvement.
Solution Approach 2:
The system uses temporary compute resources within the sequestered enclave specifically for the retraining operation. These resources are ephemeral and disposed of after use, minimizing the attack surface and reducing long-term security risks associated with persistent vulnerable systems.
3Reliability
If machine learning models are retrained on sequestered data within secure enclaves, then data confidentiality is maintained, but system complexity increases
Solution Approach 1:
The system extracts only the necessary sensitive data subsets from the larger financial data ecosystem and places them temporarily in the sequestered enclave for model retraining. This extraction minimizes the amount of data and computational resources that need to be managed securely, reducing overall system complexity.
Data Source
AI summary
An apparatus and method for training a machine learning model to generate an output using sequestered information. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to train a machine learning model on a first corpus. The memory instructs the processor to collect a second corpus, wherein the second corpus includes sequestered information. The memory instructs the processor to instantiate the machine learning model in a sequestered enclave. The memory instructs the processor to retrain the machine learning model in the sequestered enclave using the second corpus. The memory instructs the processor to receive an input from a client device. The memory instructs the processor to generate an output as a function of the input using the retrained machine learning model. The memory instructs the processor to display the output using a display device.