Data privacy has become an increasingly important issue in machine learning.
Many approaches have been developed to tackle this issue, e.g., cryptography
(Homomorphic Encryption, Differential Privacy, etc.) and collaborative training
(Secure Multi-Party Computation, Distributed Learning and Federated Learning).
These techniques have a particular focus on data encryption or secure local
computation. They transfer the intermediate information to the third-party to
compute the final result. Gradient exchanging is commonly considered to be a
secure way of training a robust model collaboratively in deep learning.
However, recent researches have demonstrated that sensitive information can be
recovered from the shared gradient. Generative Adversarial Networks (GAN), in
particular, have shown to be effective in recovering those information.
However, GAN based techniques require additional information, such as class
labels which are generally unavailable for privacy persevered learning. In this
paper, we show that, in Federated Learning (FL) system, image-based privacy
data can be easily recovered in full from the shared gradient only via our
proposed Generative Regression Neural Network (GRNN). We formulate the attack
to be a regression problem and optimise two branches of the generative model by
minimising the distance between gradients. We evaluate our method on several
image classification tasks. The results illustrate that our proposed GRNN
outperforms state-of-the-art methods with better stability, stronger
robustness, and higher accuracy. It also has no convergence requirement to the
global FL model. Moreover, we demonstrate information leakage using face
re-identification. Some defense strategies are also discussed in this work.

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