Secure Data Science
CRC Press (Verlag)
978-0-367-53410-3 (ISBN)
Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science.
After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media.
This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science.
Dr. Bhavani Thuraisingham is the Louis A. Beecherl, Jr. Distinguished Professor of Computer Science and the Executive Director of the Cyber Security Research and Education Institute (CSI) at the University of Texas at Dallas.Dr. Latifur R. Khan is currently an Associate Professor in computer science at at the University of Texas at Dallas.Dr. Murat Kantarcioglu is Professor of Computer Science and Director of the University of Texas at Dallas Data Security and Privacy Lab. His research focuses on creating technologies that can efficiently extract useful information from any data without sacrificing privacy or security. Recently, he has been working on security and privacy issues raised by data mining, privacy issues in social networks, security issues in databases, privacy issues in health care, applied cryptography for data security, risk and incentive issues in assured information sharing, use of data mining for fraud detection, botnet detection and homeland security.
Chapter 1 Introduction
PART I Supporting Technologies for Secure Data Science
Introduction to Part I
Chapter 2 Data Security and Privacy
Chapter 3 Data Mining and Security
Chapter 4 Big Data, Cloud, Semantic Web, and Social Network Technologies
Chapter 5 Big Data Analytics, Security, and Privacy
Conclusion to Part I
PART II Data Science for Cyber Security
Introduction to Part II
Chapter 6 Data Science for Malicious Executables
Chapter 7 Stream Analytics for Malware Detection
Chapter 8 Cloud-Based Data Science for Malware Detection
Chapter 9 Data Science for Insider Threat Detection
Conclusion to Part II
PART III Security and Privacy-Enhanced Data Science
Introduction to Part III
Chapter 10 Adversarial Support Vector Machine Learning
Chapter 11 Adversarial Learning Using Relevance Vector Machine Ensembles
Chapter 12 Privacy Preserving Decision Trees
Chapter 13 Toward a Privacy-Aware Policy-Based Quantified Self-Data Management Framework
Chapter 14 Data Science, COVID-19 Pandemic, Privacy, and Civil Liberties
Conclusion to Part III
PART IV Access Control and Data Science
Introduction to Part IV
Chapter 15 Secure Cloud Query Processing Based on Access Control for Big Data Systems
Chapter 16 Access Control-Based Assured Information Sharing in the Cloud
Chapter 17 Access Control for Social Network Data Management
Chapter 18 Inference and Access Control for Big Data
Chapter 19 Emerging Applications for Secure Data Science: Internet of Transportation Systems
Conclusion to Part IV
Chapter 20 Summary and Directions
Appendix A: Data Management Systems: Developments and Trends
| Erscheinungsdatum | 19.04.2022 |
|---|---|
| Zusatzinfo | 21 Tables, black and white; 110 Line drawings, black and white; 39 Halftones, black and white; 149 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 1000 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Netzwerke ► Sicherheit / Firewall | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Recht / Steuern ► Privatrecht / Bürgerliches Recht ► IT-Recht | |
| Technik ► Elektrotechnik / Energietechnik | |
| ISBN-10 | 0-367-53410-X / 036753410X |
| ISBN-13 | 978-0-367-53410-3 / 9780367534103 |
| Zustand | Neuware |
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