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005 | 20160812084310.0 | ||
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008 | 100914s2010 gw | s |||| 0|eng d | ||
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_a9783642158384 _9978-3-642-15838-4 |
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040 | _cMX-MeUAM | ||
050 | 4 | _aQA76.9.D3 | |
082 | 0 | 4 |
_a005.74 _223 |
100 | 1 |
_aDomingo-Ferrer, Josep. _eeditor. |
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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. |
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300 |
_aXI, 297p. 47 illus. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Computer Science, _x0302-9743 ; _v6344 |
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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 | ||
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_c203028 _d203028 |