Privacy in Statistical Databases : International Conference, PSD 2022, Paris, France, September 21–23, 2022, Proceedings / edited by Josep Domingo-Ferrer, Maryline Laurent

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Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Lecture Notes in Computer Science ; 13463Publisher: Cham : Springer International Publishing, 2022Publisher: Cham : Imprint: Springer, 2022Edition: 1st ed. 2022Description: 1 Online-Ressource(XI, 376 p. 98 illus., 66 illus. in color.)ISBN:

9783031139451

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Additional physical formats: 9783031139444 | 9783031139468 | Erscheint auch als: 9783031139444 Druck-Ausgabe | Erscheint auch als: 9783031139468 Druck-AusgabeDOI: DOI: 10.1007/978-3-031-13945-1Online resources:

Summary: 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 Under Differential 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 Analyses in 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.Summary: 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.PPN: PPN: 1816990590Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SCS | ZDB-2-SXCS | ZDB-2-LNC

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