Minimizing Expected Loss Under Noise for Private Models
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Solution Overview
Problem
Machine learning models trained on private data often leak information due to noise added for differential privacy, which detracts from their utility.
Innovation Solution
The method involves training a model to minimize expected loss under noise (ELUN) using a novel loss function that anticipates noise added to the weights, ensuring differential privacy while maintaining model utility by iteratively adding noise and optimizing until convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If noise is added to weights for differential privacy, then privacy protection is improved, but model utility deteriorates
Solution Approach 1:
The loss function is designed to anticipate noise before it is added to the model weights. By pre-computing the expected loss under the noise distribution and using this anticipated loss for training, the model is prepared in advance to be robust against the privacy-preserving noise that will subsequently be added, thereby maintaining utility while achieving differential privacy
Solution Approach 2:
The invention changes the parameter optimization target from the standard loss function to the expected loss under noise (ELUN). This parameter transformation allows the model to be trained on a modified objective that accounts for the noise distribution, enabling the model to achieve both differential privacy guarantees and maintained utility by optimizing for the noisy condition from the start
2Reliability
If stronger privacy guarantees are enforced, then privacy protection is improved, but model accuracy deteriorates
Solution Approach 1:
By transforming the loss function to ELUN that incorporates the noise distribution characteristics, the model learns parameters that are robust to stronger privacy guarantees. This parameter transformation enables the model to maintain accuracy even when stronger privacy measures (larger noise) are applied, as the training objective already accounts for the expected impact of such noise
3Reliability
If noise is added to weights, then differential privacy is achieved, but generalization performance deteriorates
Solution Approach 1:
The model is trained using ELUN which pre-computes the expected loss under the noise distribution. This preliminary action of anticipating noise allows the model to learn robust parameters before deployment, ensuring that when noise is added for differential privacy, the model's generalization performance remains intact because it was already optimized for noisy conditions
Data Source
AI summary
Training of a model is performed to minimize expected loss under noise (ELUN) while maintaining differential privacy. Noise is added to weights of a machine learning model as random samples drawn from a noise distribution, the noise being added in accordance with a privacy budget. The ELUN is minimized by using a loss function that anticipates noise added to the weights of the machine learning model, to find a point in the parameter space for which loss is robust to the noise in the weights. The addition of noise and the minimization of the ELUN are iterated until the weights converge and optimization constraints are satisfied. The model is utilized on arbitrary inputs while protecting the privacy of training data used to train the model.