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008			230413s2017 sz | s |||| 0|eng d
020			_a9783031023507
024	7		_a10.1007/978-3-031-02350-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aBF637.P74 _b2017 EB
100	1		_aLi, Ninghui _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686380 _c(Computer scientist)
245	1	0	_aDifferential Privacy : _bFrom Theory to Practice _cby Ninghui Li, Min Lyu, Dong Su, Weining Yang
250			_a1st edition 2017
264		1	_aCham _bSpringer International Publishing _c2017
300			_a1 recurso en línea (XIII, 124 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		_aAcknowledgments -- Introduction -- A Primer on ?-Differential Privacy -- What Does DP Mean? -- Publishing Histograms for Low-dimensional Datasets -- Differentially Private Optimization -- Publishing Marginals -- The Sparse Vector Technique -- Bibliography -- Authors' Biographies.
520			_aOver the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks. This Synthesis Lecture is the first of two volumes on differential privacy. This lecture differs from the existing books and surveys on differential privacy in that we take an approach balancing theory and practice. We focus on empirical accuracy performances of algorithms rather than asymptotic accuracy guarantees. At the same time, we try to explain why these algorithms have those empirical accuracy performances. We also take a balanced approach regarding the semantic meanings of differential privacy, explaining both its strong guarantees and its limitations. We start by inspecting the definition and basic properties of DP, and the main primitives for achieving DP. Then, we give a detailed discussion on the the semantic privacy guarantee provided by DP and the caveats when applying DP. Next, we review the state of the art mechanisms for publishing histograms for low-dimensional datasets, mechanisms for conducting machine learning tasks such as classification, regression, and clustering, and mechanisms for publishing information to answer marginal queries for high-dimensional datasets. Finally, we explain the sparse vector technique, including the many errors that have been made in the literature using it. The planned Volume 2 will cover usage of DP in other settings, including high-dimensional datasets, graph datasets, local setting, location privacy, and so on. We will also discuss various relaxations of DP.
988			_aSynthesis Collection of Technology_2017
650		7	_2embne _9140996 _aDerecho a la intimidad
650		7	_2embne _9147793 _aProtección de datos
700	1		_aLyu, Min _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688089
700	1		_aSu, Dong _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688090 _c(Computer scientist)
700	1		_aYang, Weining _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688091 _c(Computer scientist)
776	0	8	_iPrinted edition: _z9783031002359
776	0	8	_iPrinted edition: _z9783031012228
776	0	8	_iPrinted edition: _z9783031034787
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02350-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI