Machine Learning Approaches for User Identity

Dear Colleagues,

Identity theft intimidates the growth of e-commerce and online financial and government services, and a more comprehensive approach is required to protect personal information. User identity plays a critical role in building a secure, trustworthy and privacy-enhanced identity system. The state-of-the-art approaches used to manage identities cannot endure the increasing number of cyber-attacks from expert criminals. Further research is required on the secure control of identity, enhancing interoperability, and providing end users with more direct control over their digital identity. Recent advances in machine learning and artificial intelligence offer successful solutions in designing identity systems. 

The Topical Collection invites original research papers on the applications of machine learning techniques in biometrics, cyber identity, and identity management. Relevant topics include, but are not limited to:

• Machine learning and deep learning techniques in biometrics and cyber identity;
• Machine learning, neural netwoks and artificial intelligence methods in identity management;
• Privacy, convenience and security of identity systems;
• Human behavior analysis and behavior modeling;
• Continous authentication and authorization;
• Identity ecosystem for the Internet of Things (IoT);
• Adversarial attacks on user authentication;
• Adversarial learning;
• Securing the identity claim using blockchain. 

Dr. Kaushik Roy
Dr. Mustafa Atay
Dr. Ajita Rattani
Collection Editors

Manuscript Submission Information

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