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Sen Jaydip, Mayer Joceli (eds.) Information Security and Privacy in the Digital World: Some Selected Topics

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Sen Jaydip, Mayer Joceli (eds.) Information Security and Privacy in the Digital World: Some Selected Topics
ITexLi, 2023. — 155 p. — ISBN 1837681961 9781837681969 1837699801 9781837699803 183768197X 9781837681976.
In the era of generative artificial intelligence and the Internet of Things (IoT) and while there is explosive growth in the volume of data and the associated need for processing, analysis, and storage, several new challenges have arisen in identifying spurious and fake information and protecting the privacy of sensitive data. This has led to an increasing demand for more robust and resilient schemes for authentication, integrity protection, encryption, non-repudiation, and privacy preservation of data. This book presents some of the state-of-the-art research in the field of cryptography and security in computing and communications. It is a useful resource for researchers, engineers, practitioners, and graduate and doctoral students in the field of cryptography, network security, data privacy issues, and machine learning applications in the security and privacy in the context of the IoT.
Data Privacy Preservation on the Internet of Things
Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and Their Impact
Recent Results on Some Word Oriented Stream Ciphers: SNOW 1.0, SNOW 2.0 and SNOW 3G
Role of Access Control in Information Security: A Security Analysis Approach
Enhanced Hybrid Privacy Preserving Data Mining Technique
Review on Watermarking Techniques Aiming Authentication of Digital Image Artistic Works Minted as NFTs into Blockchains
Text Watermark Analysis – Concept, Technique, and Applications
Application of Computational Intelligence in Visual Quality Optimization Watermarking and Coding Tools to Improve the Medical IoT Platforms Using ECC Cybersecurity Based CoAP Protocol
Secured Storage Mechanism for the Cloud-Based Banking Applications Using ECC
Verification of Generalizability in Software Log Anomaly Detection Models
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