Privacy in Statistical Databases

Chapter 1 $$ P_{\alpha ,\beta } $$ P α , β -Privacy: A Composable Formulation of Privacy Guarantees for Data Publishing Based on Permutation

Chapter 2 $$\epsilon $$ ϵ -Differential Privacy for Microdata Releases Does Not Guarantee Confidentiality (Let Alone Utility)

Chapter 3 A Bayesian Nonparametric Approach to Differentially Private Data

Chapter 4 A Partitioned Recoding Scheme for Privacy Preserving Data Publishing

Chapter 5 Explaining Recurrent Machine Learning Models: Integral Privacy Revisited

Chapter 6 Utility-Enhancing Flexible Mechanisms for Differential Privacy

Chapter 7 Plausible Deniability

Chapter 8 Analysis of Differentially-Private Microdata Using SIMEX

Chapter 9 An Analysis of Different Notions of Effectiveness in k-Anonymity

Chapter 10 Multivariate Top-Coding for Statistical Disclosure Limitation

Chapter 11 Calculation of Risk Probabilities for the Cell Key Method

Chapter 12 On Different Formulations of a Continuous CTA Model

Chapter 13 Privacy Analysis of Query-Set-Size Control

Chapter 14 Statistical Disclosure Control When Publishing on Thematic Maps

Chapter 15 Bayesian Modeling for Simultaneous Regression and Record Linkage

Chapter 16 Probabilistic Blocking and Distributed Bayesian Entity Resolution

Chapter 17 Secure Matrix Computation: A Viable Alternative to Record Linkage?

Chapter 18 A Synthetic Supplemental Public Use File of Low-Income Information Return Data: Methodology, Utility, and Privacy Implications

Chapter 19 Integrating Differential Privacy in the Statistical Disclosure Control Tool-Kit for Synthetic Data Production

Chapter 20 Advantages of Imputation vs. Data Swapping for Statistical Disclosure Control

Chapter 21 Evaluating Quality of Statistical Disclosure Control Methods – VIOLAS Framework

Chapter 22 Detecting Bad Answers in Survey Data Through Unsupervised Machine Learning

Chapter 23 Private Posterior Inference Consistent with Public Information: A Case Study in Small Area Estimation from Synthetic Census Data

Chapter 24 Differential Privacy and Its Applicability for Official Statistics in Japan – A Comparative Study Using Small Area Data from the Japanese Population Census