LEADER 06807nam 22008775 450 001 9910484004503321 005 20251226200207.0 010 $a3-540-49332-8 024 7 $a10.1007/11930242 035 $a(CKB)1000000000283970 035 $a(SSID)ssj0000319555 035 $a(PQKBManifestationID)11937735 035 $a(PQKBTitleCode)TC0000319555 035 $a(PQKBWorkID)10339055 035 $a(PQKB)11134358 035 $a(DE-He213)978-3-540-49332-7 035 $a(MiAaPQ)EBC3068446 035 $a(PPN)123139708 035 $a(EXLCZ)991000000000283970 100 $a20100301d2006 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aPrivacy in Statistical Databases $eCENEX-SDC Project International Conference, PSD 2006, Rome, Italy, December 13-15, 2006, Proceedings /$fedited by Josep Domingo-Ferrer, Luisa Franconi 205 $a1st ed. 2006. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2006. 215 $a1 online resource (XI, 383 p.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v4302 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-540-49330-1 320 $aIncludes bibliographical references and index. 327 $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. 330 $aPrivacy in statistical databases is a discipline whose purpose is to provide - lutions to the con?ict between the increasing social, political and economical demand of accurate information, and the legal and ethical obligation to protect the privacy of the individuals and enterprises to which statistical data refer. - yond law and ethics, there are also practical reasons for statistical agencies and data collectors to invest in this topic: if individual and corporate respondents feel their privacyguaranteed,they arelikelyto providemoreaccurateresponses. There are at least two traditions in statistical database privacy: one stems from o?cial statistics, where the discipline is also known as statistical disclosure control (SDC), and the other originates from computer science and database technology.Bothstartedinthe1970s,butthe1980sandtheearly1990ssawlittle privacy activity on the computer science side. The Internet era has strengthened the interest of both statisticians and computer scientists in this area. Along with the traditional topics of tabular and microdata protection, some research lines have revived and/or appeared, such as privacy in queryable databases and protocols for private data computation. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v4302 606 $aCryptography 606 $aData encryption (Computer science) 606 $aDatabase management 606 $aComputer science$xMathematics 606 $aMathematical statistics 606 $aComputers and civilization 606 $aComputers$xLaw and legislation 606 $aInformation technology$xLaw and legislation 606 $aArtificial intelligence 606 $aCryptology 606 $aDatabase Management 606 $aProbability and Statistics in Computer Science 606 $aComputers and Society 606 $aLegal Aspects of Computing 606 $aArtificial Intelligence 615 0$aCryptography. 615 0$aData encryption (Computer science). 615 0$aDatabase management. 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics. 615 0$aComputers and civilization. 615 0$aComputers$xLaw and legislation. 615 0$aInformation technology$xLaw and legislation. 615 0$aArtificial intelligence. 615 14$aCryptology. 615 24$aDatabase Management. 615 24$aProbability and Statistics in Computer Science. 615 24$aComputers and Society. 615 24$aLegal Aspects of Computing. 615 24$aArtificial Intelligence. 676 $a005.8 701 $aDomingo-Ferrer$b Josep$01751715 701 $aFranconi$b Luisa$0116765 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484004503321 996 $aPrivacy in statistical databases$94202469 997 $aUNINA