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 |