Summary of Approximating Two-layer Relu Networks For Hidden State Analysis in Differential Privacy, by Antti Koskela
Approximating Two-Layer ReLU Networks for Hidden State Analysis in Differential Privacy
by Antti Koskela
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to private training of multi-layer neural networks under the hidden state threat model of differential privacy (DP). It demonstrates that it is possible to privately train convex problems with privacy-utility trade-offs comparable to those of one-hidden-layer ReLU networks trained with DP stochastic gradient descent (DP-SGD). The authors achieve this through a stochastic approximation of a dual formulation of the ReLU minimization problem, which results in a strongly convex problem. This enables the use of existing hidden state privacy analyses, providing accurate privacy bounds for the noisy cyclic mini-batch gradient descent (NoisyCGD) method with fixed disjoint mini-batches. The experiments on benchmark classification tasks show that NoisyCGD can achieve privacy-utility trade-offs comparable to DP-SGD applied to one-hidden-layer ReLU networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to privately train neural networks in a way that balances the need for accurate predictions with the need to keep the training process private. This is important because many real-world applications of machine learning involve sensitive data, and we don’t want attackers to be able to figure out what they can learn from our models just by looking at them. The authors use a new approach that combines ideas from convex optimization and differential privacy to get good results for both the accuracy and the privacy of their model. |
Keywords
* Artificial intelligence * Classification * Gradient descent * Machine learning * Optimization * Relu * Stochastic gradient descent
