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Privacy in Statistical Databases

International Conference, PSD 2022, Paris, France, September 21-23, 2022, Proceedings

Medium: Buch
ISBN: 978-3-031-13944-4
Verlag: Springer
Erscheinungstermin: 05.08.2022
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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.

Produkteigenschaften

  • Artikelnummer: 9783031139444
  • Medium: Buch
  • ISBN: 978-3-031-13944-4
  • Verlag: Springer
  • Erscheinungstermin: 05.08.2022
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2022
  • Serie: Lecture Notes in Computer Science
  • Produktform: Kartoniert, Paperback
  • Gewicht: 587 g
  • Seiten: 376
  • Format (B x H x T): 155 x 235 x 21 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Herausgeber

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.