Privacy and Security in Machine Learning and Artificial Intelligence
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 4254
Special Issue Editor
Special Issue Information
Dear Colleagues,
The development of Artificial Intelligence (AI) and its learning techniques, such as Machine Learning (ML) and Deep Learning (DL), have revolutionized data processing and analysis. This transformation is rapidly changing human life and has allowed for many practical applications based on AI, including the Internet of Things/Vehicles (IoT/ IoV), smart grid and energy saving, fog/edge computing, face/image recognition, text/sentimental analysis, attack detection, and healthcare.
However, the potential benefits of AI are hindered by issues such as insecurity, bias, unreliability, and privacy violations in data processing and communication. This negative impact affects both AI applications and society as a consequence. To address these concerns, this Special Issue seeks novel ideas, findings, and envisions the future of private and secure machine learning and AI.
The Special Issue will focus on and welcome submissions on topics such as privacy-preserving machine learning, deep learning, federated learning, trustworthy machine learning, metrics in private, secure and trustworthy AI, adversarial attacks against AI models, cryptography and security protocols in AI, privacy by design in AI-based systems, and applications of private, secure, and trustworthy AI.
Dr. Mina Sheikhalishahi
Guest Editor
Manuscript Submission Information
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Keywords
- privacy
- trustworthy AI
- federated learning
- AI security
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