  The Deep Leakage from Gradient (DLG) attack has emerged as a prevalent and
highly effective method for extracting sensitive training data by inspecting
exchanged gradients. This approach poses a substantial threat to the privacy of
individuals and organizations alike. This research presents a comprehensive
analysis of the gradient leakage method when applied specifically to
transformer-based models. Through meticulous examination, we showcase the
capability to accurately recover data solely from gradients and rigorously
investigate the conditions under which gradient attacks can be executed,
providing compelling evidence. Furthermore, we reevaluate the approach of
introducing additional noise on gradients as a protective measure against
gradient attacks. To address this, we outline a theoretical proof that analyzes
the associated privacy costs within the framework of differential privacy.
Additionally, we affirm the convergence of the Stochastic Gradient Descent
(SGD) algorithm under perturbed gradients. The primary objective of this study
is to augment the understanding of gradient leakage attack and defense
strategies while actively contributing to the development of privacy-preserving
techniques specifically tailored for transformer-based models. By shedding
light on the vulnerabilities and countermeasures associated with gradient
leakage, this research aims to foster advancements in safeguarding sensitive
data and upholding privacy in the context of transformer-based models.
