Privacy-Preserving Regression Tree Training via Homomorphic Encryption
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Solution Overview
Problem
Existing machine learning model training methods face challenges in ensuring data privacy and efficiency, particularly when outsourcing training or involving multi-party data, especially in heavily regulated industries like healthcare and finance.
Innovation Solution
The development of a cryptographic protocol for the non-interactive, privacy-preserving training of tree-based machine learning models using fully homomorphic encryption (FHE), which allows computations on encrypted data without revealing sensitive information, along with efficient batching options and computational optimizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is shared for model training to improve forecasting accuracy, then model performance is improved, but data privacy and security are compromised
Solution Approach 1:
A trusted third party cryptographic system serves as an intermediary between data owners and model trainers. The system uses fully homomorphic encryption to enable computations on encrypted data without requiring direct access to plaintext information, thus mediating the conflict between data utility and privacy protection
Solution Approach 2:
The patent transforms data from plaintext to ciphertext form through cryptographic encoding, changing the state parameter of data from readable to encrypted. This allows the same data to be used for training while simultaneously protecting privacy, resolving the contradiction through parameter transformation
2Reliability
If a trusted third party is used for secure data sharing, then data security is improved, but system complexity and cost increase
Solution Approach 1:
The cryptographic system operates autonomously without requiring active management or trust establishment with individual parties. Once the homomorphic encryption scheme is established, the system self-manages secure computations, reducing ongoing operational complexity despite initial setup requirements
3Reliability
If interactive training protocols are used for privacy preservation, then data security is maintained, but training efficiency and throughput decrease
Solution Approach 1:
Data is encrypted with homomorphic encryption properties before being sent to the training environment. This preliminary cryptographic preparation enables all subsequent training operations to proceed without interaction or decryption, achieving both security and efficiency through advance preparation
Solution Approach 2:
The homomorphic encryption enables continuous, uninterrupted training computations on encrypted data. The useful action of model training proceeds without interruption or interaction, maintaining both security and high throughput through uninterrupted computational flow
Data Source
AI summary
A computer implemented method can receive ciphertexts encrypted from training samples provided by one or more data owners and train a regression tree using the ciphertexts. The training is configured to encrypt the regression tree through homomorphic operations on the ciphertexts. The training is performed independently by a server without interaction with the one or more data owners.