Introduction to Privacy Preserving Data Publishing: Concepts and Techniques 1st Edition by Benjamin CM Fung ISBN

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Author: Benjamin CM Fung

“Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques” offers a comprehensive exploration of the methodologies and techniques used to protect sensitive information while still allowing for useful data analysis and sharing. In an era where data privacy is a major concern, this book addresses the growing need to balance data utility with the protection of personal and sensitive information.

The book introduces core concepts such as anonymization, data de-identification, and various privacy models including k-anonymity, l-diversity, and differential privacy. These techniques are essential for ensuring that data can be published or shared without exposing individual identities or sensitive details, thus preventing privacy breaches.

Introduction to Privacy Preserving Data Publishing: Concepts and Techniques 1st Table of contents:

• Introduction to Privacy-Preserving Data Publishing

  • 1.1 Overview of Privacy-Preserving Data Publishing
  • 1.2 Importance of Privacy in Data Sharing and Publishing
  • 1.3 Challenges in Preserving Privacy in Data
  • 1.4 Applications and Use Cases of Privacy-Preserving Data Publishing
  • 1.5 Key Concepts and Techniques in Data Privacy

• Foundations of Data Privacy

  • 2.1 Data Privacy and Security Fundamentals
  • 2.2 Types of Sensitive Data: Personally Identifiable Information (PII), Health, and Financial Data
  • 2.3 Privacy Definitions: K-anonymity, L-diversity, T-closeness
  • 2.4 Data Anonymization vs. Data De-Identification
  • 2.5 Ethical and Legal Considerations in Data Privacy

• Techniques for Privacy-Preserving Data Publishing

  • 3.1 Anonymization Techniques: Generalization and Suppression
  • 3.2 K-Anonymity: Definition, Algorithms, and Applications
  • 3.3 L-Diversity: Protecting Against Homogeneity Attacks
  • 3.4 T-Closeness: Measuring Distributional Privacy
  • 3.5 Differential Privacy: Theoretical Foundation and Implementation

• Data Sanitization and Transformation Techniques

  • 4.1 Introduction to Data Sanitization
  • 4.2 Data Transformation for Privacy Preservation
  • 4.3 Microaggregation: Grouping and Aggregating Data
  • 4.4 Perturbation Techniques for Privacy
  • 4.5 Synthetic Data Generation and Its Privacy Implications

• Advanced Privacy-Preserving Models

  • 5.1 Homomorphic Encryption: Principles and Applications
  • 5.2 Secure Multi-Party Computation
  • 5.3 Privacy-Preserving Data Mining
  • 5.4 Cryptographic Models for Privacy Preservation
  • 5.5 Blockchain Technology and Privacy

• Privacy-Preserving Techniques for Specific Data Types

  • 6.1 Privacy in Relational Databases
  • 6.2 Privacy for Text and Unstructured Data
  • 6.3 Privacy Techniques for Geospatial Data
  • 6.4 Privacy for Time-Series and Streaming Data
  • 6.5 Privacy in Graphs and Social Networks

• Evaluating the Effectiveness of Privacy-Preserving Techniques

  • 7.1 Privacy Metrics and Evaluation Criteria
  • 7.2 Quantifying Data Utility vs. Privacy Trade-Off
  • 7.3 Attacks on Privacy-Preserving Techniques
  • 7.4 Benchmarking and Comparing Privacy Models
  • 7.5 Case Studies and Real-World Evaluations

• Legal and Regulatory Aspects of Privacy-Preserving Data Publishing

  • 8.1 Data Privacy Laws and Regulations (GDPR, CCPA, HIPAA, etc.)
  • 8.2 Consent Management and Data Ownership
  • 8.3 Balancing Privacy and Data Utility in Legal Contexts
  • 8.4 Privacy in Cloud Computing and Big Data
  • 8.5 Global Privacy Frameworks and Their Impact on Data Publishing

• Applications of Privacy-Preserving Data Publishing

  • 9.1 Healthcare Data Publishing and Privacy Preservation
  • 9.2 Privacy-Preserving Techniques in Financial Data
  • 9.3 Privacy in Social Networks and Online Platforms
  • 9.4 Privacy-Preserving Data Sharing for Research and Analytics
  • 9.5 Government and Public Sector Data Privacy

• Future Trends and Challenges in Privacy-Preserving Data Publishing

  • 10.1 Emerging Techniques in Privacy-Preserving Data Publishing
  • 10.2 The Role of Artificial Intelligence and Machine Learning in Privacy Preservation
  • 10.3 Privacy in the Age of Big Data and IoT
  • 10.4 Balancing Innovation and Privacy in Data-Driven Economies
  • 10.5 Future Research Directions and Open Problems

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