LEADER 05123nam 22007935 450 001 996465941003316 005 20200705013424.0 010 $a3-642-15838-2 024 7 $a10.1007/978-3-642-15838-4 035 $a(CKB)2670000000045110 035 $a(SSID)ssj0000446711 035 $a(PQKBManifestationID)11269487 035 $a(PQKBTitleCode)TC0000446711 035 $a(PQKBWorkID)10504598 035 $a(PQKB)11673199 035 $a(DE-He213)978-3-642-15838-4 035 $a(MiAaPQ)EBC3065886 035 $z(PPN)147972329 035 $a(PPN)149031777 035 $a(EXLCZ)992670000000045110 100 $a20100914d2010 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aPrivacy in Statistical Databases$b[electronic resource] $eUNESCO Chair in Data Privacy, International Conference, PSD 2010, Corfu, Greece, September 22-24, 2010, Proceedings /$fedited by Josep Domingo-Ferrer, Emmanouil Magkos 205 $a1st ed. 2010. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2010. 215 $a1 online resource (XI, 297 p. 47 illus.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v6344 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-15837-4 320 $aIncludes bibliographical references and index. 330 $aPrivacy in statistical databases is a discipline whose purpose is to provide so- tionstothetensionbetweenthesocial,political,economicandcorporatedemand for accurate information, and the legal and ethical obligation to protect the p- vacy of the various parties involved. Those parties are the respondents (the individuals and enterprises to which the database records refer), the data o- ers (those organizations spending money in data collection) and the users (the ones querying the database or the search engine, who would like their queries to stay con?dential). Beyond law and ethics, there are also practical reasons for data-collecting agencies and corporations to invest in respondent privacy: if individual respondents feel their privacy guaranteed, they are likely to provide moreaccurateresponses. Data ownerprivacyis primarilymotivatedbypractical considerations: if an enterprise collects data at its own expense, it may wish to minimize leakage of those data to other enterprises (even to those with whom joint data exploitation is planned). Finally, user privacy results in increaseduser satisfaction, even if it may curtail the ability of the database owner to pro?le users. Thereareatleasttwotraditionsinstatisticaldatabaseprivacy,bothofwhich started in the 1970s: the ?rst one stems from o?cial statistics, where the dis- pline is also known as statistical disclosure control (SDC), and the second one originates from computer science and database technology. In o?cial statistics, the basic concern is respondent privacy. In computer science, the initial mo- vation was also respondent privacy but, from 2000 onwards, growing attention has been devoted to owner privacy (privacy-preserving data mining) and user privacy (private informationretrieval). 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v6344 606 $aDatabase management 606 $aComputer communication systems 606 $aComputer security 606 $aData encryption (Computer science) 606 $aData structures (Computer science) 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022 606 $aSystems and Data Security$3https://scigraph.springernature.com/ontologies/product-market-codes/I28060 606 $aCryptology$3https://scigraph.springernature.com/ontologies/product-market-codes/I28020 606 $aData Structures$3https://scigraph.springernature.com/ontologies/product-market-codes/I15017 606 $aData Structures and Information Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/I15009 607 $aKerkira <2010>$2swd 608 $aKongress$2swd 615 0$aDatabase management. 615 0$aComputer communication systems. 615 0$aComputer security. 615 0$aData encryption (Computer science). 615 0$aData structures (Computer science). 615 14$aDatabase Management. 615 24$aComputer Communication Networks. 615 24$aSystems and Data Security. 615 24$aCryptology. 615 24$aData Structures. 615 24$aData Structures and Information Theory. 676 $a005.8 702 $aDomingo-Ferrer$b Josep$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMagkos$b Emmanouil$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 02$aUNESCO Chair in Data Privacy. 712 12$aPSD 2010 906 $aBOOK 912 $a996465941003316 996 $aPrivacy in Statistical Databases$9772125 997 $aUNISA