Applied Deep Learning in Sensitive and Biometric Information Protection
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 September 2025 | Viewed by 602
Special Issue Editors
Interests: image processing; computer vision; pattern recognition
Special Issue Information
Dear Colleagues,
As smart devices are increasingly used in our daily lives, their importance in managing our sensitive personal data is also increasing. Regarding protecting privacy, deep learning, such as generative adversarial networks (GANs), deep neural networks (DNNs), temporal convolutional networks (TCNs), and convolutional neural networks (CNNs), stands out. These tools analyze data with high accuracy and are widely used. They detect, identify, analyze, classify, and extract features from comprehensive datasets including various smart devices and attack scenarios, achieve the accurate protection of various scenario datasets, promote secure data sharing for research and analysis, and protect personal privacy.
This Special Issue aims to study the latest progress and potential directions of deep learning in the field of sensitive information protection. Research papers and reviews in related fields are welcome. Research areas may include (but are not limited to) the following:
- Deep learning.
- Segmentation.
- Feature extraction.
- Generative adversarial networks.
- Sensitive information.
- Information security.
- Convolutional neural networks.
- Classification based on deep learning.
- Biometric information.
We look forward to receiving your contributions.
Dr. Dario Allegra
Guest Editor
Dr. Georgia Fargetta
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- deep learning
- segmentation feature extraction
- generative adversarial networks
- sensitive information
- information security
- convolutional neural networks
- classification based on deep learning
- biometric information
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