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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
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W9463084 電子資源 11.線上閱覽_V 電子書 EB QA76.9.D343 P73 2022 一般使用(Normal) 在架 0
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