Privacy in Statistical Databases

This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2022, held in Paris, France, during September 21-23, 2022.
The 25 papers presented in this volume were carefully reviewed and selected from 45 submissions. They were organized in topical sections as follows: Privacy models; tabular data; disclosure risk assessment and record linkage; privacy-preserving protocols; unstructured and mobility data; synthetic data; machine learning and privacy; and case studies.

Privacy models. - An optimization-based decomposition heuristic for the microaggregation problem. - Privacy Analysis with a Distributed Transition System and a data-wise metric. - Multivariate Mean Comparison under Differential Privacy. - Asking The Proper Question: Adjusting Queries To Statistical Procedures UnderDifferential Privacy. - Towards integrally private clustering: overlapping clusters for high privacy guarantees. - Tabular data. - Perspectives for Tabular Data Protection How About Synthetic Data? . - On Privacy of Multidimensional Data Against Aggregate Knowledge Attacks. - Synthetic Decimal Numbers as a Flexible Tool for Suppression of Post-published Tabular Data. - Disclosure risk assessment and record linkage. - The risk of disclosure when reporting commonly used univariate statistics. - Privacy-Preserving protocols. - Tit-for-Tat Disclosure of a Binding Sequence of User Analysesin Safe Data Access Centers. - Secure and non-interactive k-NN classifier using symmetric fully homomorphic encryption. - Unstructured and mobility data. - Automatic evaluation of disclosure risks of text anonymization methods. - Generation of Synthetic Trajectory Microdata from Language Models. - Synthetic data. - Synthetic Individual Income Tax Data: Methodology, Utility, and Privacy Implications. - On integrating the number of synthetic data sets m into the a priori synthesis approach . - Challenges in Measuring Utility for Fully Synthetic Data. - Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata. - Utility and Disclosure Risk for Differentially Private Synthetic Categorical Data. - Machine learning and privacy. - Membership Inference Attack Against Principal Component Analysis. - When Machine Learning Models Leak: An Exploration of Synthetic Training Data. - Case studies. - A Note on the Misinterpretation of the US Census Re-identification Attack. - A Re-examination of the Census Bureau Reconstruction and Reidentification Attack. - Quality Assessment of the 2014 to 2019 National Survey on Drug Use and Health (NSDUH) Public Use Files. - Privacy in Practice: Latest Achievements of the EUSTAT SDC group. - How Adversarial Assumptions Influence Re- identification Risk Measures: A COVID-19 Case Study.