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Handbook of Big Data Privacy

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Cover of 'Handbook of Big Data Privacy'

Table of Contents

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    Book Overview
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    Chapter 1 Big Data and Privacy: Challenges and Opportunities
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    Chapter 2 AI and Security of Critical Infrastructure
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    Chapter 3 Industrial Big Data Analytics: Challenges and Opportunities
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    Chapter 4 A Privacy Protection Key Agreement Protocol Based on ECC for Smart Grid
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    Chapter 5 Applications of Big Data Analytics and Machine Learning in the Internet of Things
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    Chapter 6 A Comparison of State-of-the-Art Machine Learning Models for OpCode-Based IoT Malware Detection
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    Chapter 7 Artificial Intelligence and Security of Industrial Control Systems
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    Chapter 8 Enhancing Network Security Via Machine Learning: Opportunities and Challenges
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    Chapter 9 Network Security and Privacy Evaluation Scheme for Cyber Physical Systems (CPS)
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    Chapter 10 Anomaly Detection in Cyber-Physical Systems Using Machine Learning
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    Chapter 11 Big Data Application for Security of Renewable Energy Resources
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    Chapter 12 Big-Data and Cyber-Physical Systems in Healthcare: Challenges and Opportunities
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    Chapter 13 Privacy Preserving Abnormality Detection: A Deep Learning Approach
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    Chapter 14 Privacy and Security in Smart and Precision Farming: A Bibliometric Analysis
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    Chapter 15 A Survey on Application of Big Data in Fin Tech Banking Security and Privacy
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    Chapter 16 A Hybrid Deep Generative Local Metric Learning Method for Intrusion Detection
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    Chapter 17 Malware Elimination Impact on Dynamic Analysis: An Experimental Machine Learning Approach
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    Chapter 18 RAT Hunter: Building Robust Models for Detecting Remote Access Trojans Based on Optimum Hybrid Features
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    Chapter 19 Active Spectral Botnet Detection Based on Eigenvalue Weighting
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Title
Handbook of Big Data Privacy
Published by
Springer International Publishing, January 2020
DOI 10.1007/978-3-030-38557-6
ISBNs
978-3-03-038556-9, 978-3-03-038557-6
Editors

Choo, Kim-Kwang Raymond, Dehghantanha, Ali

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