Data Privacy Principles and Practice 1st Edition by Nataraj Venkataramanan, Ashwin Shriram 0367841487 9780367841485

1. Introduction to Data Privacy

1.1 Introduction

1.2 What Is Data Privacy and Why Is It Important?

1.2.1 Protecting Sensitive Data

1.2.2 Privacy and Anonymity: Two Sides of the Same Coin

1.3 Use Cases: Need for Sharing Data

1.3.1 Data Mining and Analysis

1.3.2 Software Application Testing

1.3.3 Business Operations

1.4 Methods of Protecting Data

1.5 Importance of Balancing Data Privacy and Utility

1.5.1 Measuring Privacy of Anonymized Data

1.5.2 Measuring Utility of Anonymized Data

1.6 Introduction to Anonymization Design Principles

1.7 Nature of Data in the Enterprise

1.7.1 Multidimensional Data

1.7.1.1 Challenges in Privacy Preservation of Multidimensional Data

1.7.2 Transaction Data

1.7.2.1 Challenges in Privacy Preservation of Transaction Data

1.7.3 Longitudinal Data

1.7.3.1 Challenges in Anonymizing Longitudinal Data

1.7.4 Graph Data

1.7.4.1 Challenges in Anonymizing Graph Data

1.7.5 Time Series Data

1.7.5.1 Challenges in Privacy Preservation of Time Series Data

References

2. Static Data Anonymization Part I: Multidimensional Data

2.1 Introduction

2.2 Classification of Privacy Preserving Methods

2.3 Classification of Data in a Multidimensional Data Set

2.3.1 Protecting Explicit Identifiers

2.3.2 Protecting Quasi-Identifiers

2.3.2.1 Challenges in Protecting QI

2.3.3 Protecting Sensitive Data (SD)

2.4 Group-Based Anonymization

2.4.1 k-Anonymity

2.4.1.1 Why k-Anonymization?

2.4.1.2 How to Generalize Data?

2.4.1.3 Implementing k-Anonymization

2.4.1.4 How Do You Select the Value of k?

2.4.1.5 Challenges in Implementing k-Anonymization

2.4.1.6 What Are the Drawbacks of k-Anonymization?

2.4.2 l-Diversity

2.4.2.1 Drawbacks of l-Diversity

2.4.3 t-Closeness

2.4.3.1 What Is t-Closeness?

2.4.4 Algorithm Comparison

2.5 Summary

References

3. Static Data Anonymization Part II: Complex Data Structures

3.1 Introduction

3.2 Privacy Preserving Graph Data

3.2.1 Structure of Graph Data

3.2.2 Privacy Model for Graph Data

3.2.2.1 Identity Protection

3.2.2.2 Content Protection

3.2.2.3 Link Protection

3.2.2.4 Graph Metrics

3.3 Privacy Preserving Time Series Data

3.3.1 Challenges in Privacy Preservation of Time Series Data

3.3.1.1 High Dimensionality

3.3.1.2 Background Knowledge of the Adversary

3.3.1.3 Pattern Preservation

3.3.1.4 Preservation of Statistical Properties

3.3.1.5 Preservation of Frequency-Domain Properties

3.3.2 Time Series Data Protection Methods

3.3.2.1 Additive Random Noise

3.3.2.2 Perturbation of Time Series Data Using Generalization: k-Anonymization

3.4 Privacy Preservation of Longitudinal Data

3.4.1 Characteristics of Longitudinal Data

3.4.1.1 Challenges in Anonymizing Longitudinal Data

3.5 Privacy Preservation of Transaction Data

3.6 Summary

References

4. Static Data Anonymization Part III: Threats to Anonymized Data

4.1 Threats to Anonymized Data

4.2 Threats to Data Structures

4.2.1 Multidimensional Data

4.2.2 Longitudinal Data

4.2.3 Graph Data

4.2.4 Time Series Data

4.2.5 Transaction Data

4.3 Threats by Anonymization Techniques

4.3.1 Randomization (Additive)

4.3.2 k-Anonymization

4.3.3 l-Diversity

4.3.4 t-Closeness

4.4 Summary

References

5. Privacy Preserving Data Mining

5.1 Introduction

5.2 Data Mining: Key Functional Areas of Multidimensional Data

5.2.1 Association Rule Mining

5.2.1.1 Privacy Preserving of Association Rule Mining: Random Perturbation

5.2.2 Clustering

5.2.2.1 A Brief Survey of Privacy Preserving Clustering Algorithms

5.3 Summary

References

6. Privacy Preserving Test Data Manufacturing

6.1 Introduction

6.2 Related Work

6.3 Test Data Fundamentals

6.3.1 Testing

6.3.1.1 Functional Testing: System and Integration Testing

6.3.1.2 Nonfunctional Testing

6.3.2 Test Data

6.3.2.1 Test Data and Reliability

6.3.2.2 How Are Test Data Created Today?

6.3.3 A Note on Subsets

6.4 Utility of Test Data: Test Coverage

6.4.1 Privacy versus Utility

6.4.2 Outliers

6.4.3 Measuring Test Coverage against Privacy

6.5 Privacy Preservation of Test Data

6.5.1 Protecting Explicit Identifiers

6.5.1.1 Essentials of Protecting EI

6.5.1.2 What Do Tools Offer?

6.5.1.3 How Do Masking Techniques Affect Testing?

6.5.2 Protecting Quasi-Identifiers

6.5.2.1 Essentials of Protecting QI

6.5.2.2 Tool Offerings to Anonymize QI

6.5.2.3 How Does QI Anonymization Affect Test Coverage?

6.5.3 Protecting Sensitive Data (SD)

6.6 Quality of Test Data

6.6.1 Lines of Code Covered

6.6.2 Query Ability

6.6.3 Time for Testing

6.6.3.1 Test Completion Criteria

6.6.3.2 Time Factor

6.6.4 Defect Detection

6.7 Anonymization Design for PPTDM

6.8 Insufficiencies of Anonymized Test Data

6.8.1 Negative Testing

6.8.2 Sensitive Domains

6.8.3 Nonfunctional Testing

6.8.4 Regression Testing

6.8.5 Trust Deficit

6.9 Summary

References

7. Synthetic Data Generation

7.1 Introduction

7.2 Related Work

7.3 Synthetic Data and Their Use

7.4 Privacy and Utility in Synthetic Data

7.4.1 Explicit Identifiers

7.4.1.1 Privacy

7.4.1.2 Utility

7.4.1.3 Generation Algorithms

7.4.2 Quasi-Identifiers

7.4.2.1 Privacy

7.4.2.2 Utility

7.4.2.3 Generation Algorithms

7.4.3 Sensitive Data

7.4.3.1 Privacy

7.4.3.2 Utility

7.5 How Safe Are Synthetic Data?

7.5.1 Testing

7.5.1.1 Error and Exception Data

7.5.1.2 Scaling

7.5.1.3 Regression Testing

7.5.2 Data Mining

7.5.3 Public Data

7.6 Summary

References

8. Dynamic Data Protection: Tokenization

8.1 Introduction

8.2 Revisiting the Definitions of Anonymization and Privacy

8.3 Understanding Tokenization

8.3.1 Dependent Tokenization

8.3.2 Independent Tokenization

8.4 Use Cases for Dynamic Data Protection

8.4.1 Business Operations

8.4.2 Ad Hoc Reports for Regulatory Compliance

8.5 Benefits of Tokenization Compared to Other Methods

8.6 Components for Tokenization

8.6.1 Data Store

8.6.2 Tokenization Server

8.7 Summary

Reference

9. Privacy Regulations

9.1 Introduction

9.2 UK Data Protection Act 1998

9.2.1 Definitions

9.2.2 Problems in DPA

9.3 Federal Act of Data Protection of Switzerland 1992

9.3.1 Storing Patients’ Records in the Cloud

9.3.2 Health Questionnaires for Job Applicants

9.3.3 Transferring Pseudonymized Bank Customer Data Outside Switzerland

9.4 Payment Card Industry Data Security Standard (PCI DSS)

9.5 The Health Insurance Portability and Accountability Act of 1996 (HIPAA)

9.5.1 Effects of Protection

9.5.2 Anonymization Considerations

9.5.2.1 Record Owner

9.5.2.2 Business Associate

9.5.3 Anonymization Design for HIPAA

9.5.4 Notes on EIs, QIs, and SD

9.5.4.1 Explicit Identifiers

9.5.4.2 Quasi-Identifiers

9.5.4.3 Sensitive Data

9.6 Anonymization Design Checklist

9.7 Summary

9.8 Points to Ponder