1Chapter 1. Introduction
726.5 Action Plan Privacy—the Dutch Situation
21.1 What is Data privacy?
736.6 Summary
31.2 Data Sovereignty
746.7 Questions
41.3 For what reason is Data Privacy Important?
75Chapter 7. Privacy of Big Data
51.4 Data Privacy vs. Data Security
767.1 Introduction
61.5 Data Protection is the Force behind Our
777.2 Big Data
7Right to Privacy
787.3 Big Data Architecture
81.6 Various Definitions of Data Privacy
797.4 Literature Review
91.7 Data Privacy Laws and Acts
807.5 Metrics to live Data Quality
101.8 Business-Focused Data Privacy Tips
817.6 Summary of Existing Big Data Privacy
111.9 Buyers Focused Data Privacy Tips
82Preservation Models
121.10 Worldwide trends in Data Privacy
837.7 Summary
131.11 How can you achieve your compliance
847.8 Questions
14goals faster?
85Chapter 8. Privacy Models and Disclosure Risk Measures
151.12 Summary
868.1 Privacy Models
161.13 Questions
878.2 Privacy Techniques
17Chapter 2. Machine And Statistical Learning
888.3 Disclosure Risk Measures
182.1 Introduction to Machine learning
898.4 Summary
192.2 Introduction to Statistical Learning
908.5 Questions
202.3 Machine learning Vs. Statistical learning
91Chapter 9. Data Masking Methods
212.4 Summary
929.1 What is data masking?
222.5 Questions
939.2 Why is data masking important now?
23Chapter 3. Data Protection Implications
949.3 How do data masking work?
243.1 Understanding GDPR (General Data
959.4 Types of data masking
25Protection Regulation)
969.5 Common data masking techniques
263.2 How GDPR Laws Will Affect Data Collection
979.6 Data Masking Best Practices
273.3 How to Thrive in the Age of GDPR
989.7 Requirements Your Data Masking Solution
283.4 What impact does GDPR have on analytics?
99Should Fulfil
293.5 How can you drive compliance?
1009.8 What are the advantages of Data Masking?
303.6 Use GDPR as an opportunity
1019.9 Which sorts of data require data masking?
313.7 Compliant Big Data Collection Under GDPR
1029.10 How does GDPR promote data masking?
323.8 Do data subjects own their (big) data?
1039.11 What are some example data masking
333.9 Can anonymization be the solution?
104case studies?
343.10 Machine Learning and Artificial Intelligence
1059.12 What are the simplest practices of
353.11 Big Data and the GDPR
106data masking?
363.12 The Positive Implications of GDPR
1079.13 How is data masking different than
373.13 The Negative Implications of GDPR
108synthetic data?
383.14 The Aftermath of Implementation
1099.14 Data masking best practices
393.15 Big data and privacy is a crucial conversation
1109.15 Summary
403.16 Big data privacy tools: What to look for
1119.16 Questions
413.17 Summing up The Principles of the Data
112Chapter 10. Measuring The Performance Of Big Data Analytics Process
42Protection Act (DPA) 2018
11310.1 Introduction
433.18 The General Data Protection Regulation
11410.2. Related Work
44(GDPR)
11510.3 The Research Methodology
453.19 Possible Issues to Take Into Consideration
11610.4 The Performance Measures For Bda Process
463.20 Summary
11710.5 Results And Discussion
473.21 Questions
11810.6 Performance Measurement Model For
48Chapter 4. Compliance Tools
119BDA Process
494.1 Introduction
12010.7 Summary
504.2 Privacy notices
12110.8 Questions
514.3 Privacy impact assessments
122Chapter 11. Understanding and Selecting Data Masking Solutions
524.4 Privacy by design
12311.1 Introduction
534.5 Privacy seals and certification
12411.2 Data Masking
544.6 Ethical approaches
12511.3 Five Laws of Data Masking
554.7 Personal data stores
12611.4 Masks
564.8 Algorithmic transparency
12711.5 Masking Constraints
574.9 Summary
12811.6 How Masking Works
584.10 Questions
12911.7 Technical Architecture
59Chapter 5. Classification Approach for Big Data Security Based on GMPLS/MPLS Networks
13011.8 Platform Management
605.1 Introduction
13111.9 Advanced Features
615.2 Related Work
13211.10 Summary
625.3 Classification Approach
13311.11 Questions
635.4 Evaluations and Results
134Chapter 12. Big Data Forensics: Case Study of Hadoop File system
645.5 Summary
13512.1 Introduction
655.6 Questions
13612.2 Methodology
66Chapter 6. Users Privacy and Innovation
13712.3 Experimental setup
676.1 Introduction
13812.4 Results and Discussion
686.2 The concept of privacy
13912.5 Summary
696.3 Privacy and Innovation—A Conceptual
14012.6 Questions
70Framework
141Glossary
716.4 Privacy and Innovation: It Takes Two to Tango?
