Domingo-Ferrer / Laurent Privacy in Statistical Databases

International Conference, PSD 2022, Paris, France, September 21–23, 2022, Proceedings
1. Auflage 2022
ISBN: 978-3-031-13945-1
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 13463, 376 Seiten, eBook

Reihe: Lecture Notes in Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Domingo-Ferrer, Josep

Laurent, Maryline

Inhaltsverzeichnis

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.