Regression Tree Training with Fully Homomorphic Encryption
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Solution Overview
Problem
Existing machine learning model training methods face challenges in ensuring data privacy, particularly in heavily regulated industries like healthcare and finance, where outsourcing model training or sharing data with multiple parties raises privacy concerns.
Innovation Solution
The development of a privacy-preserving, non-interactive cryptographic protocol for training and inference on regression trees using fully homomorphic encryption (FHE), which allows computations on encrypted data without revealing sensitive information, along with efficient batching options and computational optimizations to enhance training throughput and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is shared for model training, then model accuracy is improved, but data privacy is compromised
Solution Approach 1:
A trusted third party cryptographic system serves as an intermediary between data owners and model trainers. The system uses homomorphic encryption to enable computations on encrypted data, allowing the model to be trained on sensitive data without exposing the actual data values. The cryptographic protocol acts as a mediator that preserves data privacy while enabling accurate model training.
2Loss of information
If a trusted third party is used for secure training, then data privacy is protected, but system complexity and cost increase
Solution Approach 1:
The cryptographic system enables data owners to independently encrypt their data and perform verification without requiring a continuously active trusted third party. The system uses self-verifying cryptographic proofs that allow parties to autonomously verify the integrity and privacy of the training process, reducing reliance on complex intermediary infrastructure.
3Loss of information
If traditional encryption methods are used, then data privacy is maintained, but computational efficiency deteriorates
Solution Approach 1:
Data is encrypted using homomorphic encryption schemes before being sent to the training environment. The encryption is performed in advance, allowing the model training to proceed on encrypted data without requiring real-time decryption. This preliminary encryption action enables efficient computation while maintaining privacy, as the heavy cryptographic operations are performed beforehand rather than during the training process.
Data Source
AI summary
A computer implemented method can receive n training samples including sample values corresponding to m attributes and respective target values (n and m are positive integers), duplicate the sample values corresponding to the m attributes, pack the sample values into ciphertexts based on a batching option, and train a regression tree using the ciphertexts. The training is configured to encrypt the regression tree through homomorphic operations on the ciphertexts.