Quantifying Privacy Guarantees for Deep Neural Network Inference via Hammersley-Chapman-Robbins Bounds

Core Concepts

Adding noise to the activations (features) in the final layers of deep neural networks can limit the quality of possible reconstructions of the input data. The Hammersley-Chapman-Robbins (HCR) bounds provide a principled way to quantify the confidentiality arising from such added noise.

Abstract

The paper studies the privacy preservation arising from adding noise to the activations (features) in the final layers of deep neural networks. The Hammersley-Chapman-Robbins (HCR) bounds provide a way to quantify the confidentiality of the input data based on the variance of any unbiased estimator for reconstructing the inputs.
The key highlights and insights are:

The HCR bounds provide easily interpretable, tight data-driven guarantees on confidentiality by lower bounding the variance of any estimator for reconstructing the inputs.
Numerical experiments on MNIST and CIFAR-10 datasets indicate that the HCR bounds are on the precipice of being effective for small neural networks, but appear insufficient on their own to guarantee confidentiality for larger models like ResNet-18 and Swin-T pre-trained on ImageNet-1000.
Supplementing the addition of noise to features with other methods for providing confidentiality may be warranted for larger models like ImageNet, as the HCR bounds alone are not strong enough.
The results limit consideration to amounts of added noise that incur little degradation in the accuracy of classification from the noisy features, so the added noise enhances confidentiality without much reduction in task performance.

Stats

The paper does not contain any explicit numerical data or statistics to support the key arguments. The analysis is based on theoretical HCR bounds and results from numerical experiments on standard image classification datasets and neural network architectures.

Quotes

The paper does not contain any striking quotes supporting the key arguments.

Key Insights Distilled From

Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds

by Kamalika Cha... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02866.pdf

Deeper Inquiries

How can the HCR bounds be further strengthened or combined with other privacy-preserving techniques to provide stronger confidentiality guarantees for larger neural network models

To strengthen the HCR bounds for larger neural network models, one approach could be to combine them with differential privacy techniques. Differential privacy adds noise to the data itself, providing an additional layer of protection. By integrating differential privacy mechanisms with the HCR bounds, the overall confidentiality guarantees can be enhanced. Additionally, employing advanced encryption methods and secure multi-party computation protocols can further bolster the privacy of the data during inference with large neural network models. These techniques ensure that even if an adversary gains access to the model or the data, the information remains secure and confidential.

What are the potential trade-offs between the level of noise added to features and the accuracy degradation on the target task

The trade-offs between the level of noise added to features and the accuracy degradation on the target task are crucial considerations in privacy-preserving machine learning. Increasing the amount of noise added to features typically improves privacy by making it harder to reconstruct the original data. However, this can lead to a reduction in the accuracy of the model's predictions. Finding the optimal balance between privacy and accuracy involves conducting thorough experiments to determine the impact of different noise levels on model performance. Techniques like differential privacy offer a principled way to optimize these trade-offs by quantifying the privacy guarantees and accuracy trade-offs based on the level of noise added.

Can these trade-offs be optimized in a principled way

While the findings of the paper are specific to image classification tasks, the principles and methodologies discussed can be applied to other machine learning domains such as natural language processing (NLP) and speech recognition. In NLP, for example, adding noise to word embeddings or text features can help protect sensitive information in text data. Similarly, in speech recognition, introducing noise to audio features can enhance privacy during inference. The concept of adding noise to features and quantifying confidentiality using bounds like HCR can be generalized across various machine learning applications, with adjustments made to suit the specific characteristics and requirements of each domain.