Add to ORKGPrivacy-preserving is a key problem for the machine learning algorithm. Spiking neural network (SNN) plays an important role in many domains, such as image classification, object detection, and speech recognition, but the study on the privacy protection of SNN is urgently needed. This study combines the differential privacy (DP) algorithm and SNN and proposes differentially private spiking neural network (DPSNN). DP injects noise into the gradient, and SNN transmits information in discrete spike trains so that our differentially private SNN can maintain strong privacy protection while still ensuring high accuracy. We conducted experiments on MNIST, Fashion-MNIST, and the face recognition dataset Extended YaleB. When the privacy protection i...
State-of-the-art approaches for training Differentially Private (DP) Deep Neural Networks (DNN) face...
We study the privacy implications of training recurrent neural networks (RNNs) with sensitive traini...
One barrier to more widespread adoption of differentially private neural networks is the entailed ac...
Data is the key to information mining that unveils hidden knowledge. The ability to revealed knowled...
We study a pitfall in the typical workflow for differentially private machine learning. The use of d...
With the increasing popularity of graph neural networks (GNNs) in several sensitive applications lik...
The rapid development of artificial intelligence has brought considerable convenience, yet also intr...
Recently, deep neural networks (DNNs) have achieved exciting things in many fields. However, the DNN...
Using machine learning to improve health care has gained popularity. However, most research in machi...
Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunate...
Graph Neural Networks (GNNs) are a popular technique for modelling graph-structured data and computi...
Differentially Private methods for training Deep Neural Networks (DNNs) have progressed recently, in...
Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation ...
We study the privacy risks that are associated with training a neural network's weights with self-su...
Does a neural network's privacy have to be at odds with its accuracy? In this work, we study the eff...
State-of-the-art approaches for training Differentially Private (DP) Deep Neural Networks (DNN) face...
We study the privacy implications of training recurrent neural networks (RNNs) with sensitive traini...
One barrier to more widespread adoption of differentially private neural networks is the entailed ac...
Data is the key to information mining that unveils hidden knowledge. The ability to revealed knowled...
We study a pitfall in the typical workflow for differentially private machine learning. The use of d...
With the increasing popularity of graph neural networks (GNNs) in several sensitive applications lik...
The rapid development of artificial intelligence has brought considerable convenience, yet also intr...
Recently, deep neural networks (DNNs) have achieved exciting things in many fields. However, the DNN...
Using machine learning to improve health care has gained popularity. However, most research in machi...
Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunate...
Graph Neural Networks (GNNs) are a popular technique for modelling graph-structured data and computi...
Differentially Private methods for training Deep Neural Networks (DNNs) have progressed recently, in...
Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation ...
We study the privacy risks that are associated with training a neural network's weights with self-su...
Does a neural network's privacy have to be at odds with its accuracy? In this work, we study the eff...
State-of-the-art approaches for training Differentially Private (DP) Deep Neural Networks (DNN) face...
We study the privacy implications of training recurrent neural networks (RNNs) with sensitive traini...
One barrier to more widespread adoption of differentially private neural networks is the entailed ac...