Data Gathering, Analysis and Protection of Privacy Through Randomized Response Techniques: Qualitative and Quantitative Human Traits

 
 
North Holland (Verlag)
  • 1. Auflage
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  • erschienen am 20. April 2016
  • |
  • 544 Seiten
 
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978-0-444-63571-6 (ISBN)
 

Data Gathering, Analysis and Protection of Privacy through Randomized Response Techniques: Qualitative and Quantitative Human Traits tackles how to gather and analyze data relating to stigmatizing human traits. S.L. Warner invented RRT and published it in JASA, 1965. In the 50 years since, the subject has grown tremendously, with continued growth. This book comprehensively consolidates the literature to commemorate the inception of RR.


  • Brings together all relevant aspects of randomized response and indirect questioning
  • Tackles how to gather and analyze data relating to stigmatizing human traits
  • Gives an encyclopedic coverage of the topic
  • Covers recent developments and extrapolates to future trends
0169-7161
  • Englisch
  • Oxford
  • |
  • Niederlande
Elsevier Science
  • 12,92 MB
978-0-444-63571-6 (9780444635716)
0444635718 (0444635718)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Data Gathering, Analysis and Protection of Privacy Through Randomized Response Techniques: Qualitative and Quantitative Human Traits
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Chapter 1: Review of Certain Recent Advances in Randomized Response Techniques
  • 1. Introduction
  • 2. Warner's and Related Techniques
  • 3. Cryptographic RRT
  • 4. Reverse RRT
  • 5. Certain Recent Theoretical and Practical Results
  • 5.1. Unified Theory
  • 5.2. Stratification and RRT
  • 5.3. Cramér-Rao Lower Bound
  • 5.4. Game Theory and RRT
  • 5.5. Smart Phones and RRT
  • 5.6. Alternatives to RRT
  • 5.7. Meta Analysis
  • 6. Epilogue
  • Acknowledgment
  • References
  • Chapter 2: The Background and Genesis of Randomized Response Techniques
  • References
  • Chapter 3: How Randomized Response Techniques Need not Be Confined to Simple Random Sampling but Liberally Applicable to G ...
  • 1. Introduction
  • 2. Two Prominent RR Devices Revised for General Applications
  • 2.1. Warner Stanley's (1965) Device
  • 2.2. Simmon's RR Device Revised
  • 3. Quantitative RRs
  • 4. Protection of Privacy
  • 4.1. When a Characteristic Is Qualitative and SRSWR Is Allowed
  • 4.2. When a General Sampling Design Is Allowed to Cover a Qualitative Characteristic
  • 4.3. Protection of Privacy Covering Quantitative Variables
  • 5. Optional RR Techniques
  • References
  • Chapter 4: The Classical Randomized Response Techniques
  • 1. Introduction
  • 2. Warner's Randomized Response Technique
  • 3. The Unrelated Question Model
  • 4. Reading Warner (1965) and Greenberg et al. (1969) 50 Years Later
  • 5. Epilogue
  • References
  • Chapter 5: On the Estimation of Correlation Coefficient Using Scrambled Responses
  • 1. Introduction
  • 2. Two Scrambling Variable Randomized Response Technique
  • 3. Scrambling Variables Are Dependent
  • 4. Estimation of the Correlation Coefficient ?xy
  • 5. Bias and Mean Squared Error of rxy
  • 6. Scrambling Variables Are Independent
  • 7. Bias and Mean Square Error of r1
  • 8. Single Scrambling Variable Randomized Response Technique
  • 9. Bias and Mean Squared Error of r2
  • 10. Correlation Between Sensitive and Nonsensitive Variable
  • 11. Bias and Mean Square Error of r3
  • 12. Simulation Study
  • Acknowledgments
  • Appendix
  • References
  • Chapter 6: Admissible and Optimal Estimation in Finite Population Sampling Under Randomized Response Models
  • 1. Introduction
  • 2. Notations and Preliminaries
  • 3. Estimation Based on Single RR
  • 3.1. Nonexistence of a Best Estimator
  • 3.2. Admissibility Results
  • 3.3. Optimality Results
  • 4. Estimation Based on Independent Multiple Responses
  • 4.1. Nonexistence of a Best Estimator
  • 4.2. Admissibility Results
  • 4.3. Optimality Results
  • 5. Concluding Remarks
  • References
  • Chapter 7: A Mixture of True and Randomized Responses in the Estimation of the Number of People Having a Certain Attribute
  • 1. Introduction
  • 2. A General RR Technique for the Estimation of Group Size
  • 3. Combining True and Randomized Responses
  • 4. A Vivid Illustration of This Strategy Including True and Masked Responses
  • References
  • Chapter 8: Estimation of Complex Population Parameters Under the Randomized Response Theory
  • 1. Introduction
  • 2. Foundations of Functional Linearization
  • 3. Functional Linearization with the RR Technique
  • 4. Some Simple Examples
  • 5. Further Examples with Some Inequality Indices
  • 6. Final Remarks
  • Acknowledgments
  • References
  • Chapter 9: An Efficient Randomized Response Model Using Two Decks of Cards Under Simple and Stratified Random Sampling
  • 1. Introduction
  • 2. Efficient Randomized Response Model Under Simple Random Sampling
  • 2.1. The Proposed Model
  • 2.2. Efficiency Comparison
  • 3. Simulation Study
  • 4. Efficient Randomized Response Model Under Stratified Random Sampling
  • 4.1. The Proposed Stratified Model
  • 4.2. Efficiency Comparison
  • 5. Double Sampling for the Proposed Stratified Model
  • 6. Conclusion
  • References
  • Chapter 10: Software for Randomized Response Techniques
  • 1. Introduction
  • 2. Software for Helping to Conduct a Survey with RR
  • 3. Software for the Estimation with Data Obtained Using RR Techniques
  • 4. Summary
  • Acknowledgments
  • References
  • Chapter 11: Poststratification Based on the Choice of Use of a Quantitative Randomization Device
  • 1. Introduction
  • 2. Poststratification Based on the Choice of a Quantitative Randomization Device
  • 3. Relative Efficiency
  • References
  • Chapter 12: Variance Estimation in Randomized Response Surveys
  • 1. Introduction
  • 2. Variance of Horvitz-Thompson (1952) Estimator
  • 3. Variance of Hansen-Hurwitz (1943) Estimator
  • 4. Variance of Raj's (1956) Ordered Estimator
  • 5. Variance of Murthy's (1957) Unordered Estimator
  • 6. Variance of Ratio Estimator Based on Lahiri (1951), Midzuno (1952), and Sen's (1953) Sampling Scheme
  • 7. Variance of Hartley-Ross (1954) Unbiased Ratio-Type Estimator
  • References
  • Chapter 13: Behavior of Some Scrambled Randomized Response Models Under Simple Random Sampling, Ranked Set Sampling and Ra ...
  • 1. Introduction
  • 2. Scrambling Procedures
  • 3. Behavior Under RHC Unequal Probability Model
  • 4. Behavior Under RSS
  • 5. Conclusions
  • Acknowledgments
  • References
  • Chapter 14: Estimation of a Finite Population Variance Under Linear Models for Randomized Response Designs
  • 1. Introduction
  • 2. Optimal Estimation of V
  • 3. UMVU Estimation
  • References
  • Chapter 15: Randomized Response and New Thoughts on Politz-Simmons Technique
  • 1. Introduction
  • 2. Not-at-Home's
  • 2.1. Randomized Response Hartley-Politz-Simmons [RR-HPS] Technique
  • 2.2. A New RR-HPS Technique
  • Acknowledgments
  • Appendix
  • References
  • Chapter 16: Optional Randomized Response: A Critical Review
  • 1. Introduction
  • 1.1. Warner's Technique: The Pioneering Method
  • 1.2. Ericksson's Technique
  • 1.3. A More General Model
  • 2. General Method of Estimation
  • 3. Optional Randomized Response Techniques
  • 3.1. Full ORT
  • 3.1.1. Arnab (2004)
  • 3.1.1.1. A Few Important Sampling Strategies
  • 3.1.1.1.1. Horvitz-Thompson Estimator Based on a Fixed Sample Size Design
  • 3.1.1.1.2. Simple Random Sampling Without Replacement
  • 3.1.1.1.3. Rao-Hartley-Cochran Sampling Design
  • 3.1.1.1.4. Probability Proportional to Size with Replacement Sampling Design
  • 3.1.1.1.5. Simple Random Sampling with Replacement
  • 3.1.2. Chaudhuri and Saha (2005)
  • 3.1.3. Chang and Huang (2001)
  • 3.1.4. Huang (2008)
  • 3.1.4.1. Method I
  • 3.1.4.2. Method II
  • 3.2. Partial ORT
  • 3.2.1. Pal (2008)
  • 3.2.2. Mangat and Singh (1994)
  • 3.2.3. Gupta et al. (2002)
  • 3.2.4. Gupta et al. (2010)
  • 3.2.5. Huang (2010)
  • 4. Efficiency of the ORT
  • 5. Conclusion
  • References
  • Chapter 17: A Concise Theory of Randomized Response Techniques for Privacy and Confidentiality Protection
  • 1. Introduction
  • 2. Vital Attributes of Randomization Experiments
  • 3. Statistical Estimation for Fixed P
  • 4. Estimation Under Invariant Post-randomization
  • 5. Assessing Privacy and Confidentiality Protection
  • 6. Discussion
  • Acknowledgments
  • References
  • Chapter 18: A Review of Regression Procedures for Randomized Response Data, Including Univariate and Multivariate Logistic ...
  • 1. Introduction
  • 2. Univariate and Multivariate RR Data, No Explanatory Variables
  • 2.1. Theory
  • 2.2. Estimation
  • 3. Logistic Regression of Univariate RR Data
  • 3.1. Theory
  • 3.2. Estimation
  • 3.3. Extensions
  • 3.3.1. Repeated Cross Sectional RR Data
  • 3.3.2. Repeated Cross Sectional RR Data with a Changing RR Design
  • 3.3.3. RR for Explanatory Variable
  • 4. Extensions of Regression Approaches to Multivariate RR Data
  • 4.1. The Multivariate Logistic Regression Model Proposed by Glonek and McCullagh
  • 4.2. Proportional Odds Model
  • 5. Models Including Self-Protective Responses
  • 5.1. Item Response Models
  • 5.2. Self-Protective Responses
  • 5.3. Example
  • 5.4. Remaining Issues
  • References
  • Chapter 19: Eliciting Information on Sensitive Features: Block Total Response Technique and Related Inference
  • 1. Randomized Response Technique
  • 2. Block Total Response Technique
  • 3. SBTRM : Use of BIBD and Complimentary BIBD
  • 4. Relative Comparison of BIBD-Based SBTRMs
  • 5. Deriving the EB Estimators
  • 6. Illustrative Example
  • 7. Concluding Remarks
  • Acknowledgment
  • Appendix
  • A.1. Illustrative Examples
  • A.1.1. Illustrative Example 1
  • A.1.2. Illustrative Example 2
  • A.1.3. Illustrative Example 3
  • References
  • Chapter 20: Optional Randomized Response Revisited
  • 1. Introduction
  • 2. Early Work
  • 3. Scrambled Response
  • 4. General Sampling Designs
  • 5. Concluding Remarks
  • Acknowledgments
  • References
  • Chapter 21: Measures of Respondent Privacy in Randomized Response Surveys
  • 1. Introduction
  • 2. Qualitative Stigmatizing Variable
  • 3. Quantitative Stigmatizing Variable
  • 3.1. Continuous Stigmatizing Variable
  • 3.2. Discrete Stigmatizing Variable
  • 4. Concluding Remarks
  • References
  • Chapter 22: Cramer-Rao Lower Bounds of Variance for Estimating Two Proportions and Their Overlap by Using Two Decks of Cards
  • 1. Introduction
  • 1.1. Simple Model
  • 1.2. Crossed Model
  • 2. Cramer-Rao Lower Bounds of Variances for the Simple Model
  • 3. Cramer-Rao Lower Bounds of Variances for the Crossed Model
  • 4. Comparison of the Variances and Lower Bounds
  • 5. Unique Estimates
  • 6. Range Restricted Maximum Likelihood Estimates
  • Acknowledgment
  • Appendix A
  • Appendix B. Codes Used in Simulation Studies
  • References
  • Chapter 23: Estimating a Finite Population Proportion Bearing a Sensitive Attribute from a Single Probability Sample by It ...
  • 1. Introduction
  • 2. Item Count Technique Using a Single Sample
  • 3. An Alternative Estimator of VP?A
  • 4. Numerical Presentation
  • 5. Conclusion
  • Questionnaire 1
  • Questionnaire 2
  • Acknowledgments
  • References
  • Chapter 24: Surveying a Varying Probability Adaptive Sample to Estimate Cost of Hospital Treatments of Sensitive Diseases b...
  • 1. Introduction
  • 2. Formulation of Problem
  • 3. RR Surveys
  • 4. Adaptive Cluster Sampling
  • 4.1. Network
  • 5. Revised Adaptive Randomized Response Surveys
  • 6. Simulation Study
  • 7. Concluding Remarks
  • References
  • Chapter 25: Estimation of Means of Two Rare Sensitive Characteristics: Cramer-Rao Lower Bound of Variances
  • 1. Introduction
  • 2. Estimation of Two Rare Sensitive Attributes
  • 3. Proposed Randomized Response Model for Two Rare Sensitive Attributes
  • 4. Relative Efficiency
  • Acknowledgments
  • Appendix
  • References
  • Chapter 26: Estimating Sensitive Population Proportion by Generating Randomized Response Following Direct and Inverse Hype ...
  • 1. Introduction
  • 2. Generating RR by Hypergeometric Distribution
  • 3. Generating RR by Negative Hypergeometric Distribution
  • 4. Comparative Efficiencies of Inverse Hypergeometric vs Direct Hypergeometric RR Generation for Different Sampling Schemes
  • 4.1. SRSWR in n Draws
  • 4.2. SRSWOR in n Draws
  • 4.3. PPSWR in n Draws
  • 4.4. Rao, Hartley, and Cochran's Sampling of Size n
  • 4.5. Midzuno's (1952) Sampling of n Persons
  • 4.6. Comparison of the Efficiencies
  • 5. Numerical Illustration Showing Relative Performances by Simulation
  • 6. Concluding Remarks
  • Acknowledgment
  • References
  • Chapter 27: Incredibly Efficient Use of a Negative Hypergeometric Distribution in Randomized Response Techniques
  • 1. Introduction
  • 2. Singh and Sedory Randomization Device
  • 3. Proposed Incredibly Efficient Randomization Device
  • 4. Efficiency Comparison
  • 5. Limiting Case with Four Decks of Cards
  • 6. Relative Efficiency of the Limiting Case
  • Acknowledgments
  • Appendix
  • References
  • Chapter 28: Comparison of Different Imputing Methods for Scrambled Responses
  • 1. Introduction
  • 2. Ratio Method of Imputing Scrambled Responses
  • 3. Regression Method of Imputing Scrambled Responses
  • 4. Imputing Scrambled Responses Using Higher Order Moments of An Auxiliary Variable
  • 5. Some Useful Results
  • 6. Properties of Different Estimators
  • 7. Application to a Real Data Set
  • Acknowledgments
  • References
  • Chapter 29: On an Indirect Response Model
  • 1. Preliminaries
  • 2. Introducing the Model
  • 2.1. Method of Moments
  • 2.2. Maximum Likelihood or Pseudolikelihood Method
  • 2.3. Bayesian Framework
  • Acknowledgments
  • References
  • Index
  • Back Cover

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