001449555 000__ 05802cam\a2200637\a\4500
001449555 001__ 1449555
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001449555 019__ $$a1344490910
001449555 020__ $$a9783031139451$$q(electronic bk.)
001449555 020__ $$a3031139453$$q(electronic bk.)
001449555 020__ $$z9783031139444
001449555 020__ $$z3031139445
001449555 0247_ $$a10.1007/978-3-031-13945-1$$2doi
001449555 035__ $$aSP(OCoLC)1344538533
001449555 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dGW5XE$$dEBLCP$$dYDX$$dOCLCQ$$dOCLCF$$dOCLCQ
001449555 049__ $$aISEA
001449555 050_4 $$aQA76.9.D343
001449555 08204 $$a005.8$$223/eng/20220923
001449555 1112_ $$aPSD (Conference : 2004- )$$d(2022 :$$cParis, France)
001449555 24510 $$aPrivacy in statistical databases :$$bInternational Conference, PSD 2022, Paris, France, September 21-23, 2022, Proceedings /$$cJosep Domingo-Ferrer, Maryline Laurent (eds.).
001449555 2463_ $$aPSD 2022
001449555 260__ $$aCham :$$bSpringer,$$c2022.
001449555 300__ $$a1 online resource (375 pages)
001449555 336__ $$atext$$btxt$$2rdacontent
001449555 337__ $$acomputer$$bc$$2rdamedia
001449555 338__ $$aonline resource$$bcr$$2rdacarrier
001449555 4901_ $$aLecture Notes in Computer Science ;$$v13463
001449555 500__ $$a3.2 Problem Definition
001449555 500__ $$aIncludes author index.
001449555 5050_ $$aIntro -- Preface -- Organization -- Contents -- Privacy Models -- An Optimization-Based Decomposition Heuristic for the Microaggregation Problem -- 1 Introduction -- 2 The Decomposition Heuristic -- 3 The Local Search Improvement Heuristic -- 4 The Mixed Integer Linear Optimization Algorithm Based on Column Generation -- 5 Computational Results -- 6 Conclusions -- References -- Privacy Analysis with a Distributed Transition System and a Data-Wise Metric -- 1 Introduction -- 2 Distributed Labeled-Tagged Transition Systems -- 3 -Indistinguishability, -Local-Differential Privacy
001449555 5058_ $$a4 -Differential Privacy -- 5 Comparing Two Nodes on One or More Runs -- 6 New Metric for Indistinguishability and DP -- 7 Related Work and Conclusion -- References -- Multivariate Mean Comparison Under Differential Privacy -- 1 Introduction -- 2 Mathematical Background -- 2.1 Statistical Tests for Two Samples -- 2.2 Hotelling's t2-Test -- 2.3 Differential Privacy -- 3 Privatized Mean Comparison -- 3.1 Privatization of the t2-Statistic -- 3.2 Bootstrap -- 4 Simulation -- 5 Conclusion -- A Proofs -- B Effects of Privatization -- Example -- C Algorithms -- References
001449555 5058_ $$aAsking the Proper Question: Adjusting Queries to Statistical Procedures Under Differential Privacy -- 1 Introduction -- 1.1 Setting -- 1.2 Our Contribution -- 1.3 Related Work -- 2 Fixed (Non-random) Datasets -- 2.1 Confidence Regions -- 2.2 Testing Hypotheses: Likelihood-Ratio Test -- 3 Random, Normally Distributed Data -- 3.1 Confidence Regions -- 3.2 Testing Hypotheses: Likelihood-Ratio Test -- 4 Numerical Example -- 5 Appendix -- References -- Towards Integrally Private Clustering: Overlapping Clusters for High Privacy Guarantees -- 1 Introduction -- 2 Preliminaries -- 2.1 Integral Privacy
001449555 5058_ $$a2.2 k-Anonymity, Microaggregation, and MDAV -- 2.3 Genetic Algorithms -- 3 -Centroid c-Means -- 3.1 Formalization -- 3.2 Properties -- 4 Experiments -- 4.1 Solving the Optimization Problem -- 4.2 Datasets -- 4.3 Parameters -- 4.4 Results -- 5 Conclusions -- References -- II Tabular Data -- Perspectives for Tabular Data Protection -- How About Synthetic Data? -- 1 Introduction -- 2 Recalling the Methods under Consideration -- 2.1 Synthetic Data -- 2.2 Targeted Record Swapping (TRS) -- 2.3 CKM Noise Design for Tabulations of Continuous Variables -- 3 Study Design -- 3.1 Test Data
001449555 5058_ $$a3.2 Application Settings for Synthetic Data Generation -- 3.3 Application Settings for Targeted Record Swapping -- 3.4 Application Settings for the Cell Key Method -- 4 Measuring Utility and Disclosure Risk -- 5 Results -- 5.1 Comparing Utility Loss -- 6 Summary, Open Issues, Conclusions -- Appendix -- A.1 Approximate Behavior of Utility Loss Indicator U for CKM in Extremely Detailed Tabulations -- Appendix A.2 -- Appendix A.3 -- References -- On Privacy of Multidimensional Data Against Aggregate Knowledge Attacks -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 3.1 Preliminaries
001449555 506__ $$aAccess limited to authorized users.
001449555 520__ $$aThis book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2022, held in Paris, France, during September 21-23, 2022. The 25 papers presented in this volume were carefully reviewed and selected from 45 submissions. They were organized in topical sections as follows: Privacy models; tabular data; disclosure risk assessment and record linkage; privacy-preserving protocols; unstructured and mobility data; synthetic data; machine learning and privacy; and case studies.
001449555 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 23, 2022).
001449555 650_0 $$aDatabase security$$vCongresses.
001449555 650_0 $$aData protection$$vCongresses.
001449555 650_0 $$aStatistics$$xDatabases$$vCongresses.
001449555 655_0 $$aElectronic books.
001449555 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001449555 7001_ $$aDomingo-Ferrer, Josep.
001449555 7001_ $$aLaurent-Maknavicius, Maryline.
001449555 77608 $$iPrint version:$$aDomingo-Ferrer, Josep.$$tPrivacy in Statistical Databases.$$dCham : Springer International Publishing AG, ©2022$$z9783031139444
001449555 830_0 $$aLecture notes in computer science ;$$v13463.
001449555 852__ $$bebk
001449555 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-13945-1$$zOnline Access$$91397441.1
001449555 909CO $$ooai:library.usi.edu:1449555$$pGLOBAL_SET
001449555 980__ $$aBIB
001449555 980__ $$aEBOOK
001449555 982__ $$aEbook
001449555 983__ $$aOnline
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