?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.
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