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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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Mentioned by

3 tweeters


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58 Mendeley
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Handbook of Big Data Privacy
Published by
Springer International Publishing, January 2020
DOI 10.1007/978-3-030-38557-6
978-3-03-038556-9, 978-3-03-038557-6

Choo, Kim-Kwang Raymond, Dehghantanha, Ali

Twitter Demographics

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 14%
Unspecified 6 10%
Student > Bachelor 5 9%
Student > Master 5 9%
Student > Doctoral Student 4 7%
Other 10 17%
Unknown 20 34%
Readers by discipline Count As %
Computer Science 17 29%
Unspecified 6 10%
Engineering 6 10%
Business, Management and Accounting 4 7%
Social Sciences 2 3%
Other 4 7%
Unknown 19 33%