Privacy and Security Issues in Big Data: An Analytical View on Business Intelligence (Services and Business Process Reengineering)

Preface
Contents
About the Editors
1 Security in Big Data: A Succinct Survey
	1 Introduction
	2 Big Data Security
	3 Background Study
	4 Solution to security in Big Data
	5 Analysis of Different Privacy-Preserving Techniques
	6 Big Data Security in Agriculture and Farming
	7 Conclusion
	References
2 Big Data-Driven Privacy and Security Issues and Challenges
	1 Introduction
	2 Big Data and Their Characteristics
	3 Big Data-Driven Security
	4 Some Imperative Security Issues and Challenges
		4.1 AI Applications and Big Data Security
		4.2 Fake Data Generation and Fake Mappers
		4.3 Fake Mappers
		4.4 Granular Access Control
		4.5 Data Provenance
		4.6 Real-Time Big Data Analytics Security Concerns
		4.7 Big Data and IoT Security Concerns
		4.8 Cloud and Big Data Security Concerns
		4.9 Summary
	5 Big Data-Driven Privacy
		5.1 Some Good Measures
		5.2 Challenges and Recommendations
	6 Research in the Big Data-Driven Privacy and Security
		6.1 Some Good Concerns Related to Big Data-Driven Privacy and Security Research
	References
3 Big Data Process-Based Security and Privacy Issues and Measures
	1 Introduction
	2 Challenges in Applying Security Over Big Data
		2.1 Data Source
		2.2 Data Mapper
		2.3 Data Warehouse
	3 Proposed Algorithm to Eliminate Fake Data
	4 Data Analysis
	5 Feature Extraction
		5.1 Feature Extraction with Moment Functions
	6 Feature Extraction with Neural Networks
	7 Feature Extraction with Clustering
	8 Conclusion
	References
4 Exploring and Presenting Security Measures in Big Data Paradigm
	1 Introduction
		1.1 What Is Big Data?
		1.2 Examples of Big Data
		1.3 Different Forms of Big Data
		1.4 Characteristics of Big Data
	2 Privacy in Big Data
		2.1 Privacy Threats in Big Data
		2.2 Privacy Issues in Big Data
		2.3 Privacy Strategies in Big Data
		2.4 Big Data Privacy Tool Features
		2.5 Big Data Privacy Preservation Techniques
	3 Security in Big Data
		3.1 Security Issues in Big Data
		3.2 Security Challenges in Big Data
		3.3 Security Techniques for Big Data
	4 Conclusion
	References
5 Comparative Analysis of Anonymization Techniques
	1 Introduction
	2 Anonymization of Data
	3 Prevailing Illustrations of Data Anonymization
	4 Data Anonymization Techniques
	5 Comparative Analysis
	6 Conclusion and Future Work
	References
6 Standardization of Big Data and Its Policies
	1 Introduction
	2 Conventional Data Privacy Standards Versus Big Data Privacy
	3 The Drive for Self-Service and the Necessity for the Protection of Big Data
		3.1 Privacy and Security Issues
		3.2 Privacy Standards in a Big Data World
		3.3 Privacy of Big Data in the Data Production Process
		3.4 Privacy of Big Data in the Data Storage Phase
		3.5 Privacy of Big Data in Processing of Data
	4 New Privacy Security Approaches in Big Data
		4.1 Differential Privacy
		4.2 Identity-Based Anonymization
		4.3 Apriori Algorithm Privacy-Preservation Algorithm Within the MapReduce Framework
		4.4 Privacy-Preserving Publishing Big Data
		4.5 Improving the k-anonymity and I-diversity Privacy Model
	5 Global Data Privacy Initiatives
	6 Access, Confirmation, and Correction Rights
		6.1 Rights to Objection, Restriction, and Portability
		6.2 The Right to Be Forgotten
		6.3 Cross-Border Transfer of Personal Data
		6.4 Benefits of Enforcement
		6.5 Building an AI Ecosystem
	7 Brief Review of Indian Personal Data Protection Bill, 2018s
	8 Conclusion
	References
7 Privacy-Preserving Cryptographic Model for Big Data Analytics
	1 Introduction
		1.1 Big-Data Privacy and Security
	2 Related Work
		2.1 Hadoop Secure Map Reduce Model
	3 Proposed Cryptography-Based Environment
		3.1 Data Encryption Standard
		3.2 RSA Algorithm
		3.3 Encryption and Decryption at Multiple Levels
	4 Implementation of MLED
	5 Experimental Result of the Cryptosystems
	6 Conclusion and Future Scope
	References
8 Application of Big Data Analytics in Healthcare Industry Along with Its Security Issues
	1 Introduction
	2 Background
		2.1 History of Big Data Analytics
		2.2 Architecture of Big Data Analytics
		2.3 Capability of Big Data Analytics
		2.4 Conceptualization of the Potential Benefits in the Use of Big Data Analytics
	3 Research Methodology
		3.1 Case Collection
		3.2 Research Approach and Procedure
	4 The Strategies to Get Success in Big Data Analytics
		4.1 Importance of Big Data Analytics in the Healthcare Industry
		4.2 Big Data Ecosystem Designed for Healthcare
	5 Potential Application Areas
		5.1 Big Data Used for Medical Image Processing
		5.2 Medical Signal Analytics
		5.3 Genomics: Emerging Big Data Applications
	6 Privacy Versus Security in Health Care
		6.1 Issues Related to Security and Privacy
		6.2 Solutions
	7 Limitation and Future Research
	8 Conclusion
	References
9 An Analytical Perspective of Machine Learning in Cybersecurity
	1 Introduction
	2 The Security Concerns
	3 Existing Methods of Security
	4 Role of Machine Learning in Dealing with Security Breaches
	5 Challenges ML Faces in Cybersecurity
	6 Demonstration of an Intelligent Cybersecurity Model
		6.1 Security Data Collection
		6.2 Security Data Preparation
		6.3 Machine Learning-Based Security Modeling
		6.4 Incremental Learning and Dynamism
	7 Common Machine Learning Algorithms that Are Used in Cybersecurity
		7.1 Supervised Learning
		7.2 Unsupervised Learning
		7.3 Neural Networks and Deep Learning
		7.4 Other Learning Techniques
	8 Deep Learning Methods in Cybersecurity
		8.1 Malware
		8.2 Domain Generation Algorithms and Botnet Detection
		8.3 Drive-by Download Attacks
		8.4 File Type Identification
		8.5 Organization Traffic Identification
		8.6 SPAM Identification
		8.7 Border Gateway Protocol Anomaly Detection
		8.8 User Authentication
	9 Conclusion
	References
10 Business Intelligence Influenced Customer Relationship Management in Telecommunication Industry and Its Security Challenges
	1 Introduction
	2 Role of Big Data in Business Intelligence
	3 CRM and BI in Telecommunication Industry
	4 Security Challenges Pertinent to CRM and Telecommunication Industry
		4.1 Preventive Measures Against the Security Issues
		4.2 Proposed Framework
	5 Conclusion
	References
11 Data Protection and Data Privacy Act for BIG DATA Governance
	1 Introduction
	2 Data Protection and Data Privacy Act
		2.1 Introduction to Data Protection and Data Privacy Act
		2.2 Personal Data
	3 Data Protection
		3.1 Purpose of Data Protection
		3.2 Principles of Data Protection
		3.3 Available Data Protection and Data Privacy Acts
		3.4 Current Data Protection Practices
	4 Data Privacy
		4.1 Privacy Policy
		4.2 Data Protection Through Data Security
		4.3 Data Security Technologies for Data Protection
		4.4 Existing Data Protection and Data Privacy Acts Policies Acts for Big Data
	5 Current Data Security Challenges in Big Data Protection
		5.1 Challenges in Using Internet of Things (IoT)
		5.2 Challenges in Using Hadoop Technology
		5.3 Challenges in Using Cloud Computing
	6 Data Security Technologies for Data Protection and Data Privacy of Big Data
	References