iDLG: Improved Deep Leakage from Gradients (2001.02610v1)

Abstract: It is widely believed that sharing gradients will not leak private training data in distributed learning systems such as Collaborative Learning and Federated Learning, etc. Recently, Zhu et al. presented an approach which shows the possibility to obtain private training data from the publicly shared gradients. In their Deep Leakage from Gradient (DLG) method, they synthesize the dummy data and corresponding labels with the supervision of shared gradients. However, DLG has difficulty in convergence and discovering the ground-truth labels consistently. In this paper, we find that sharing gradients definitely leaks the ground-truth labels. We propose a simple but reliable approach to extract accurate data from the gradients. Particularly, our approach can certainly extract the ground-truth labels as opposed to DLG, hence we name it Improved DLG (iDLG). Our approach is valid for any differentiable model trained with cross-entropy loss over one-hot labels. We mathematically illustrate how our method can extract ground-truth labels from the gradients and empirically demonstrate the advantages over DLG.

• The paper introduces iDLG, which overcomes DLG limitations by accurately extracting ground-truth labels using gradient sign analysis.
• The method fixes the label during gradient matching, leading to more stable and efficient data reconstruction with lower MSE.
• Experiments on MNIST, CIFAR-100, and LFW demonstrate iDLG's 100% label extraction accuracy, highlighting critical privacy vulnerabilities in distributed learning.

iDLG: Improved Deep Leakage from Gradients

The paper "iDLG: Improved Deep Leakage from Gradients" addresses a significant issue in distributed learning systems, namely the potential leakage of private training data from shared gradients. It builds upon the work of Zhu et al. with their Deep Leakage from Gradient (DLG) framework, and proposes an enhanced approach termed Improved DLG (iDLG) which claims to reliably extract ground-truth labels with high accuracy from the gradients shared during collaborative or federated learning processes.

Background and Motivation

Distributed learning paradigms like Collaborative Learning and Federated Learning hinge on the principle that sharing gradients instead of actual data can protect participants' private information. However, recent advancements have shown that it's possible to reconstruct private data from these shared gradients. The original DLG method aimed at reconstructing data by matching dummy gradients to shared gradients. Despite its novel approach, DLG struggled with convergence issues and inconsistent recovery of true labels, which limited its effectiveness.

Methodology

The iDLG method overcomes these limitations by leveraging mathematical insights to accurately deduce ground-truth labels from shared gradients. It exploits the properties of gradients with respect to classification loss in neural networks, particularly those trained with cross-entropy loss. The authors derive a crucial insight: the gradient associated with the correct label activation is consistently negative, whereas gradients associated with incorrect labels are positive. This distinct behavior allows iDLG to accurately identify the ground-truth label, which DLG failed to achieve consistently.

Once the label is correctly extracted, the improved method optimizes the reconstruction of data with better accuracy and fidelity. By fixing the label, iDLG simplifies the gradient matching optimization process, resulting in more stable and efficient data recovery.

Experimental Validation and Results

A comprehensive set of experiments was conducted on MNIST, CIFAR-100, and LFW datasets to evaluate the iDLG method. The results demonstrate a definitive improvement over the original DLG approach. Notably, iDLG achieved 100% accuracy in label extraction across all datasets, a significant leap from the 79.1%-89.9% range documented for DLG. Furthermore, the fidelity of recovered data with iDLG was consistently higher, as evidenced by lower mean square error (MSE) values between original and dummy data.

Implications and Future Directions

The research highlights critical privacy vulnerabilities in distributed learning frameworks. The ability of iDLG to consistently extract ground-truth labels from gradients raises substantial concerns about data confidentiality in collaborative and federated learning settings. Practically, it underscores the necessity for enhanced privacy-preserving techniques and gradient obfuscation mechanisms.

From a theoretical perspective, the work provides a deeper understanding of the relationship between gradient behavior and label information during neural network training. This insight opens avenues for further research into more robust privacy-preserving techniques, potentially influencing the development of adapted learning algorithms that mitigate information leakage risks.

Future developments in AI could aim to incorporate cryptographic methods or differential privacy approaches into the gradient-sharing process to counteract the vulnerabilities exposed by iDLG. As distributed learning becomes increasingly prevalent, ensuring data privacy will be pivotal in fostering trust and compliance within collaborative AI frameworks.