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003			ES-MaUEC
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008			230413s2016 sz | s |||| 0|eng d
020			_a9783031023477
024	7		_a10.1007/978-3-031-02347-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aHF5548.37 _b2016 EB
100	1		_aDomingo-Ferrer, Josep _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688084
245	1	0	_aDatabase Anonymization : _bPrivacy Models, Data Utility, and Microaggregation-based Inter-model Connections _cby Josep Domingo-Ferrer, David Sánchez, Jordi Soria-Comas
250			_a1st edition 2016
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XV, 120 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Information Security Privacy and Trust _x1945-9750
505	0		_aPreface -- Acknowledgments -- Introduction -- Privacy in Data Releases -- Anonymization Methods for Microdata -- Quantifying Disclosure Risk: Record Linkage -- The k-Anonymity Privacy Model -- Beyond k-Anonymity: l-Diversity and t-Closeness -- t-Closeness Through Microaggregation -- Differential Privacy -- Differential Privacy by Multivariate Microaggregation -- Differential Privacy by Individual Ranking Microaggregation -- Conclusions and Research Directions -- Bibliography -- Authors' Biographies .
520			_aThe current social and economic context increasingly demands open data to improve scientific research and decision making. However, when published data refer to individual respondents, disclosure risk limitation techniques must be implemented to anonymize the data and guarantee by design the fundamental right to privacy of the subjects the data refer to. Disclosure risk limitation has a long record in the statistical and computer science research communities, who have developed a variety of privacy-preserving solutions for data releases. This Synthesis Lecture provides a comprehensive overview of the fundamentals of privacy in data releases focusing on the computer science perspective. Specifically, we detail the privacy models, anonymization methods, and utility and risk metrics that have been proposed so far in the literature. Besides, as a more advanced topic, we identify and discuss in detail connections between several privacy models (i.e., how to accumulate the privacy guarantees they offer to achieve more robust protection and when such guarantees are equivalent or complementary); we also explore the links between anonymization methods and privacy models (how anonymization methods can be used to enforce privacy models and thereby offer ex ante privacy guarantees). These latter topics are relevant to researchers and advanced practitioners, who will gain a deeper understanding on the available data anonymization solutions and the privacy guarantees they can offer.
988			_aSynthesis Collection of Technology_2016
650		7	_2embne _9147793 _aProtección de datos
650		7	_2embne _9672206 _aBases de datos _xMedidas de seguridad
700	1		_aSánchez, David _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688085 _c(Computer scientist)
700	1		_aSoria-Comas, Jordi _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688086
776	0	8	_iPrinted edition: _z9783031012198
776	0	8	_iPrinted edition: _z9783031034756
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02347-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI