Summary of Guarantees Of Confidentiality Via Hammersley-chapman-robbins Bounds, by Kamalika Chaudhuri et al.
Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds
by Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar, Mark Tygert
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computers and Society (cs.CY); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces a method for protecting privacy during inference with deep neural networks by adding noise to activations in the last layers prior to the final classifiers or task-specific layers. This added noise prevents reconstruction of inputs from noisy features, ensuring confidentiality. The study presents convenient and computationally tractable bounds using Hammersley and Chapman-Robbins inequalities (HCR bounds) for small neural nets on MNIST and CIFAR-10 datasets. Numerical experiments indicate that the HCR bounds are effective for small networks but insufficient for larger ones like ResNet-18 and Swin-T pre-trained on ImageNet-1000. The results show that adding noise enhances confidentiality without significantly degrading classification accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Protecting privacy during inference with deep neural networks is a big problem. Imagine you’re trying to keep someone’s private information safe while they’re using a machine learning model. One way to do this is by adding “noise” to the data before it gets analyzed. This noise makes it harder for someone to figure out what the original data was. The researchers in this paper look at how well this works and find that it can be very effective, especially with small networks. They also show that bigger networks might need something extra to keep things private. |
Keywords
» Artificial intelligence » Classification » Inference » Machine learning » Resnet
