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Privacy in statistical databases = International Conference, PSD 2022, Paris, France, September 21-23, 2022 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Privacy in statistical databases/ edited by Josep Domingo-Ferrer, Maryline Laurent.
其他題名:	International Conference, PSD 2022, Paris, France, September 21-23, 2022 : proceedings /
其他題名:	PSD 2022
其他作者:	Domingo-Ferrer, Josep.
團體作者:	PSD (Conference : 2004- )
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xi, 376 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Database security - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-031-13945-1
ISBN:	9783031139451

Privacy in statistical databases = International Conference, PSD 2022, Paris, France, September 21-23, 2022 : proceedings /
Privacy in statistical databasesInternational Conference, PSD 2022, Paris, France, September 21-23, 2022 : proceedings /[electronic resource] :PSD 2022edited by Josep Domingo-Ferrer, Maryline Laurent. - Cham :Springer International Publishing :2022. - xi, 376 p. :ill., digital ;24 cm. - Lecture notes in computer science,134631611-3349 ;. - Lecture notes in computer science ;13463..

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.

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.

ISBN: 9783031139451

Standard No.: 10.1007/978-3-031-13945-1doiSubjects--Topical Terms:

738112
Database security
--Congresses.

LC Class. No.: QA76.9.D343 / P73 2022

Dewey Class. No.: 005.8

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