Non-Interactive Decision Tree Evaluation via Homomorphic Encryption
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Solution Overview
Problem
Existing machine learning models, such as decision trees, face challenges in preserving the privacy of both the input data and the model itself, particularly due to vulnerabilities in white-box and black-box access that can lead to model inversion attacks compromising the privacy of training data.
Innovation Solution
A client-server protocol using homomorphic encryption allows for the evaluation of a decision tree on encrypted data without decryption, ensuring that only the classification result is revealed to the client, while maintaining the privacy of both the decision tree and the client's input data by employing a BGV-type homomorphic encryption scheme and ciphertext packing techniques to manage noise and optimize computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the decision tree model is made accessible for evaluation, then the classification service can be provided, but the model structure and training data may be exposed through model inversion attacks
Solution Approach 1:
Homomorphic encryption acts as an intermediary between the decision tree model and the input data, allowing the model to process encrypted data without exposing either the model structure or the plaintext data. The encryption scheme enables computations on ciphertexts, serving as a mediator that preserves privacy while enabling functionality.
Solution Approach 2:
The patent transforms the evaluation process by changing the parameter state of the data from plaintext to encrypted form. By operating in the encrypted domain, the system maintains model privacy while providing classification services, effectively changing the operational parameters of the evaluation process.
2Loss of information
If the input data is encrypted to protect privacy, then model privacy is maintained, but the computational complexity and overhead increase
Solution Approach 1:
The homomorphic encryption is applied preliminarily to the input data before it reaches the model evaluation stage. This preliminary encryption action enables subsequent processing without decryption, avoiding the need for complex decryption-encryption operations during the evaluation process itself.
Solution Approach 2:
The patent replaces the traditional mechanical decryption-evaluation-encryption process with a homomorphic encryption-based system that performs evaluations directly on ciphertexts. This substitution eliminates the need for decryption operations, reducing computational overhead despite the initial encryption complexity.
3Loss of information
If the decision tree is evaluated on encrypted data, then privacy is preserved, but the evaluation time and computational resources increase
Solution Approach 1:
The patent substitutes traditional decryption-based evaluation mechanics with homomorphic encryption-based computation. By replacing the decryption step with homomorphic operations, the system preserves privacy while enabling direct computation on encrypted data, though computational resources are differently allocated.
Data Source
AI summary
A non-interactive protocol is provided for evaluating machine learning models such as decision trees. A client can delegate the evaluation of a machine learning model such as a decision tree to a server by sending an encrypted input and receiving only the encryption of the result. The inputs can be encoded using their binary representation. Efficient data representations are then combined with different algorithmic optimizations to keep the computational overhead and the communication cost low. Related apparatus, systems, techniques and articles are also described.