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020 _a9783642158384
_9978-3-642-15838-4
040 _cMX-MeUAM
050 4 _aQA76.9.D3
082 0 4 _a005.74
_223
100 1 _aDomingo-Ferrer, Josep.
_eeditor.
245 1 0 _aPrivacy in Statistical Databases
_h[recurso electrónico] :
_bUNESCO Chair in Data Privacy, International Conference, PSD 2010, Corfu, Greece, September 22-24, 2010. Proceedings /
_cedited by Josep Domingo-Ferrer, Emmanouil Magkos.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _aXI, 297p. 47 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_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 _aComputer science.
650 0 _aComputer Communication Networks.
650 0 _aData protection.
650 0 _aData structures (Computer science).
650 0 _aData encryption (Computer science).
650 0 _aDatabase management.
650 1 4 _aComputer Science.
650 2 4 _aDatabase Management.
650 2 4 _aComputer Communication Networks.
650 2 4 _aSystems and Data Security.
650 2 4 _aData Encryption.
650 2 4 _aData Structures.
650 2 4 _aData Structures, Cryptology and Information Theory.
700 1 _aMagkos, Emmanouil.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642158377
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v6344
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-15838-4
596 _a19
942 _cLIBRO_ELEC
999 _c203028
_d203028