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005 20240730190033.0
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008 100301s2006 gw | s |||| 0|eng d
020 _a9783540493327
_9978-3-540-49332-7
024 7 _a10.1007/11930242
_2doi
050 4 _aQA268
072 7 _aGPJ
_2bicssc
072 7 _aURY
_2bicssc
072 7 _aCOM083000
_2bisacsh
072 7 _aGPJ
_2thema
072 7 _aURY
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082 0 4 _a005.824
_223
245 1 0 _aPrivacy in Statistical Databases
_h[electronic resource] :
_bCENEX-SDC Project International Conference, PSD 2006, Rome, Italy, December 13-15, 2006, Proceedings /
_cedited by Josep Domingo-Ferrer, Luisa Franconi.
250 _a1st ed. 2006.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2006.
300 _aXI, 383 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInformation Systems and Applications, incl. Internet/Web, and HCI,
_x2946-1642 ;
_v4302
505 0 _aMethods for Tabular Protection -- A Method for Preserving Statistical Distributions Subject to Controlled Tabular Adjustment -- Automatic Structure Detection in Constraints of Tabular Data -- A New Approach to Round Tabular Data -- Harmonizing Table Protection: Results of a Study -- Utility and Risk in Tabular Protection -- Effects of Rounding on the Quality and Confidentiality of Statistical Data -- Disclosure Analysis for Two-Way Contingency Tables -- Statistical Disclosure Control Methods Through a Risk-Utility Framework -- A Generalized Negative Binomial Smoothing Model for Sample Disclosure Risk Estimation -- Entry Uniqueness in Margined Tables -- Methods for Microdata Protection -- Combinations of SDC Methods for Microdata Protection -- A Fixed Structure Learning Automaton Micro-aggregation Technique for Secure Statistical Databases -- Optimal Multivariate 2-Microaggregation for Microdata Protection: A 2-Approximation -- Using the Jackknife Method to Produce Safe Plots of Microdata -- Combining Blanking and Noise Addition as a Data Disclosure Limitation Method -- Why Swap When You Can Shuffle? A Comparison of the Proximity Swap and Data Shuffle for Numeric Data -- Adjusting Survey Weights When Altering Identifying Design Variables Via Synthetic Data -- Utility and Risk in Microdata Protection -- Risk, Utility and PRAM -- Distance Based Re-identification for Time Series, Analysis of Distances -- Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk -- Using Mahalanobis Distance-Based Record Linkage for Disclosure Risk Assessment -- Improving Individual Risk Estimators -- Protocols for Private Computation -- Single-Database Private Information Retrieval Schemes : Overview, Performance Study, and Usage with Statistical Databases -- Privacy-Preserving Data Set Union.-"Secure" Log-Linear and Logistic Regression Analysis of Distributed Databases -- Case Studies -- Measuring the Impact of Data Protection Techniques on Data Utility: Evidence from the Survey of Consumer Finances -- Protecting the Confidentiality of Survey Tabular Data by Adding Noise to the Underlying Microdata: Application to the Commodity Flow Survey -- Italian Household Expenditure Survey: A Proposal for Data Dissemination -- Software -- The ARGUS Software in CENEX -- Software Development for SDC in R -- On Secure e-Health Systems -- IPUMS-International High Precision Population Census Microdata Samples: Balancing the Privacy-Quality Tradeoff by Means of Restricted Access Extracts.
520 _aPrivacy in statistical databases is a discipline whose purpose is to provide - lutions to the con?ict between the increasing social, political and economical demand of accurate information, and the legal and ethical obligation to protect the privacy of the individuals and enterprises to which statistical data refer. - yond law and ethics, there are also practical reasons for statistical agencies and data collectors to invest in this topic: if individual and corporate respondents feel their privacyguaranteed,they arelikelyto providemoreaccurateresponses. There are at least two traditions in statistical database privacy: one stems from o?cial statistics, where the discipline is also known as statistical disclosure control (SDC), and the other originates from computer science and database technology.Bothstartedinthe1970s,butthe1980sandtheearly1990ssawlittle privacy activity on the computer science side. The Internet era has strengthened the interest of both statisticians and computer scientists in this area. Along with the traditional topics of tabular and microdata protection, some research lines have revived and/or appeared, such as privacy in queryable databases and protocols for private data computation.
650 0 _aCryptography.
_91973
650 0 _aData encryption (Computer science).
_99168
650 0 _aDatabase management.
_93157
650 0 _aComputer science
_xMathematics.
_93866
650 0 _aMathematical statistics.
_99597
650 0 _aComputers and civilization.
_921733
650 0 _aComputers
_xLaw and legislation.
_975151
650 0 _aInformation technology
_xLaw and legislation.
_982531
650 0 _aArtificial intelligence.
_93407
650 1 4 _aCryptology.
_931769
650 2 4 _aDatabase Management.
_93157
650 2 4 _aProbability and Statistics in Computer Science.
_931857
650 2 4 _aComputers and Society.
_931668
650 2 4 _aLegal Aspects of Computing.
_953952
650 2 4 _aArtificial Intelligence.
_93407
700 1 _aDomingo-Ferrer, Josep.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9141139
700 1 _aFranconi, Luisa.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9141140
710 2 _aSpringerLink (Online service)
_9141141
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540493303
776 0 8 _iPrinted edition:
_z9783540832959
830 0 _aInformation Systems and Applications, incl. Internet/Web, and HCI,
_x2946-1642 ;
_v4302
_9141142
856 4 0 _uhttps://doi.org/10.1007/11930242
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