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999			_c387252 _d387252
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003			ES-MaUEC
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007			cr nn 008mamaa
008			220601s2010 sz | s |||| 0|eng d
020			_a9783031018343
024	7		_a10.1007/978-3-031-01834-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aHF5548.37 _b2010 EB
100	1		_aWong, Raymond Chi-Wing _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686997
245	1	0	_aPrivacy-Preserving Data Publishing : _bAn Overview _cby Raymond Chi-Wing Wong, Ada Wai-Chee Fu
250			_a1st edition 2010
264		1	_aCham _bSpringer International Publishing _c2010
300			_a1 recurso en línea (IX, 128 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Data Management _x2153-5426
505	0		_aIntroduction -- Fundamental Concepts -- One-Time Data Publishing -- Multiple-Time Data Publishing -- Graph Data -- Other Data Types -- Future Research Directions.
520			_aPrivacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data analysis, privacy-preserving techniques are often required to reduce the possibility of identifying sensitive information about individuals. For example, in medical data, sensitive information can be the fact that a particular patient suffers from HIV. In spatial data, sensitive information can be a specific location of an individual. In web surfing data, the information that a user browses certain websites may be considered sensitive. Consider a dataset containing some sensitive information is to be released to the public. In order to protect sensitive information, the simplest solution is not to disclose the information. However, this would be an overkill since it will hinder the process of data analysis over the data from which we can find interesting patterns. Moreover, in some applications, the data must be disclosed under the government regulations. Alternatively, the data owner can first modify the data such that the modified data can guarantee privacy and, at the same time, the modified data retains sufficient utility and can be released to other parties safely. This process is usually called as privacy-preserving data publishing. In this monograph, we study how the data owner can modify the data and how the modified data can preserve privacy and protect sensitive information. Table of Contents: Introduction / Fundamental Concepts / One-Time Data Publishing / Multiple-Time Data Publishing / Graph Data / Other Data Types / Future Research Directions.
988			_aSynthesis Collection of Technology_2010
650		7	_2embne _9162648 _aData mining
650		7	_2embne _9672206 _aBases de datos _xMedidas de seguridad
650		7	_2embne _9147793 _aProtección de datos
700	1		_aFu, Ada _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686998
776	0	8	_iPrinted edition: _z9783031007064
776	0	8	_iPrinted edition: _z9783031029622
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01834-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI