000 04256nam a22006375i 4500
001 978-3-642-15838-4
003 DE-He213
005 20240423125556.0
007 cr nn 008mamaa
008 100914s2010 gw | s |||| 0|eng d
020 _a9783642158384
_9978-3-642-15838-4
024 7 _a10.1007/978-3-642-15838-4
_2doi
050 4 _aQA76.9.D3
072 7 _aUN
_2bicssc
072 7 _aCOM021000
_2bisacsh
072 7 _aUN
_2thema
082 0 4 _a005.74
_223
245 1 0 _aPrivacy in Statistical Databases
_h[electronic resource] :
_bUNESCO Chair in Data Privacy, International Conference, PSD 2010, Corfu, Greece, September 22-24, 2010, Proceedings /
_cedited by Josep Domingo-Ferrer, Emmanouil Magkos.
250 _a1st ed. 2010.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2010.
300 _aXI, 297 p. 47 illus.
_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 ;
_v6344
520 _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).
650 0 _aDatabase management.
650 0 _aComputer networks .
650 0 _aData protection.
650 0 _aCryptography.
650 0 _aData encryption (Computer science).
650 0 _aArtificial intelligence
_xData processing.
650 0 _aData structures (Computer science).
650 0 _aInformation theory.
650 1 4 _aDatabase Management.
650 2 4 _aComputer Communication Networks.
650 2 4 _aData and Information Security.
650 2 4 _aCryptology.
650 2 4 _aData Science.
650 2 4 _aData Structures and Information Theory.
700 1 _aDomingo-Ferrer, Josep.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMagkos, Emmanouil.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783642158377
776 0 8 _iPrinted edition:
_z9783642158391
830 0 _aInformation Systems and Applications, incl. Internet/Web, and HCI,
_x2946-1642 ;
_v6344
856 4 0 _uhttps://doi.org/10.1007/978-3-642-15838-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cSPRINGER
999 _c179455
_d179455