Sustainable Development Using Private AI

Security Models and Applications
1. Auflage 2024
ISBN: 978-1-032-71672-5
Verlag: CRC Press

Buch, Englisch, 318 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 646 g

Reihe: Artificial Intelligence for Sustainable Engineering and Management

This book covers the fundamental concepts of private AI and its applications. It also covers fusion of Private AI with cutting-edge technologies like cloud computing, federated learning and computer vision.

Security Models and Applications for Sustainable Development Using Private AI reviews various encryption algorithms used for providing security in private AI. It discusses the role of training machine learning and Deep learning technologies in private AI. The book provides case studies of using private AI in various application areas such as purchasing, education, entertainment, medical diagnosis, predictive care, conversational personal assistants, wellness apps, early disease detection, and recommendation systems. The authors provide additional knowledge to handling the customer’s data securely and efficiently. It also provides multi-model dataset storage approaches along with the traditional approaches like anonymization of data and differential privacy mechanisms.

The target audience includes undergraduate and postgraduate students in Computer Science, Information technology, Electronics and Communication Engineering and related disciplines.  This book is also a one stop reference point for professionals, security researchers, scholars, various government agencies and security practitioners, and experts working in the cybersecurity Industry specifically in the R & D division.

Zielgruppe

Postgraduate and Professional Reference

Autoren/Hrsg.

Maheswari V, Uma

Aluvalu, Rajanikanth

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit

Inhaltsverzeichnis

1. A Research Study on Concepts & Applications of Artificial Intelligence: Governance in Smart Cities. 2. Encryption and Decryption Algorithms in Private AI. 3. Advancing Privacy in AI: Homomorphic Encryption and Private AI. 4. AI-Driven Privacy Preservation using Homomorphic Encryption with AM-ResNetbased Classification in Gastrointestinal Diseases. 5. Cryptographic Security in Credit Card Fraud Detection Using Homomorphic Encryption with CRO based Hybrid BL-GRU Classification. 6. Private AI in Education: A Critical Challenges and Aspects of Enhancement Strategies. 7. A model of pre-adoptive appraisal toward private AI implementation in Public Sector Accounting Education in Higher Education Institutions. 8. Recruitment and Staffing in educational sectors via Explainable AI and Blockchain. 9. Private AI in Healthcare: Technological Constraints, Future Directions and Emerging Trends. 10. Unlocking the Potential of Deep Learning in Knee Bone Cancer Diagnosis UsingMSCSA-Net Segmentation and MLGC-LTNet Classification. 11. Enhancing Image Forgery Detection on Social Media via Grabcut Segmentation and RA based MobileNet with MREA for Data Security. 12. Private AI in E-Commerce: Safeguarding Consumer Data in the Digital Marketplace. 13. Private Artificial Intelligence (AI) in Social Media. 14. Blockchain based PrivateAI Model with RPOA based Sampling Method for Credit Card Fraud Detection. 15. Breast Cancer Detection using Mother Optimization Algorithm based Chaotic Map with Private AI Model

Über Autor(innen)

Uma Maheswari V is Senior Member of IEEE and working as an Associate Professor, Department of CSE, Chaitanya Bharathi Institute of Technology, Hyderabad, India.

Rajanikanth Aluvalu is a Senior Member of IEEE and working as Director and Professor, Symbiosis Institute of Technology, Hyderabad Campus, Hyderabad, Symbiosis International (Deemed University), Pune, India. Post Doctoral Research Fellow, COPE labs, Lusófona University, Portugal, Member, Artificial Intelligence Group, Department of Computer Engineering, Lusófona University, Portugal.