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024	7		_a10.1007/978-3-031-13945-1 _2doi
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245	1	0	_aPrivacy in Statistical Databases _h[electronic resource] : _bInternational Conference, PSD 2022, Paris, France, September 21–23, 2022, Proceedings / _cedited by Josep Domingo-Ferrer, Maryline Laurent.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_aXI, 376 p. 98 illus., 66 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Computer Science, _x1611-3349 ; _v13463
505	0		_aPrivacy 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 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.
520			_aThis 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.
650		0	_aData mining.
650		0	_aDatabase management.
650		0	_aMachine learning.
650		0	_aComputers and civilization.
650		0	_aData protection.
650		0	_aComputer networks .
650	1	4	_aData Mining and Knowledge Discovery.
650	2	4	_aDatabase Management.
650	2	4	_aMachine Learning.
650	2	4	_aComputers and Society.
650	2	4	_aData and Information Security.
650	2	4	_aComputer Communication Networks.
700	1		_aDomingo-Ferrer, Josep. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aLaurent, Maryline. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031139444
776	0	8	_iPrinted edition: _z9783031139468
830		0	_aLecture Notes in Computer Science, _x1611-3349 ; _v13463
856	4	0	_uhttps://doi.org/10.1007/978-3-031-13945-1
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