Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

Training deep learning models with differential privacy (DP) results in a degradation of
performance. The training dynamics of models with DP show a significant difference from …

Recent works have shown that selecting an optimal model architecture suited to the
differential privacy setting is necessary to achieve the best possible utility for a given privacy …

To alleviate the utility degradation of deep learning image classification with differential
privacy (DP) employing extra public data or pre-trained models has been widely explored …

Differential privacy (DP) has achieved remarkable results in the field of privacy-preserving
machine learning. However, existing DP frameworks do not satisfy all the conditions for …

Abstract Background and Objective: Concerns about patient privacy issues have limited the
application of medical deep learning models in certain real-world scenarios. Differential …

Differential privacy (DP), as a rigorous mathematical definition quantifying privacy leakage,
has become a well-accepted standard for privacy protection. Combined with powerful …

In this article, we present a detailed review of current practices and state-of-the-art
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …

In recent years, differential privacy has seen significant advancements in image
classification; however, its application to video activity recognition remains under-explored …

Differential privacy is a widely accepted measure of privacy in the context of deep learning
algorithms, and achieving it relies on a noisy training approach known as differentially …