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008			201128s2021 gw a o |||| 0|eng d
020			_a9783030588960
024	7		_a10.1007/978-3-030-58896-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.A25 _b2021 EB
100	1		_aLiu, Alex X. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678835
245	1	0	_aAlgorithms for Data and Computation Privacy _cby Alex X Liu, Rui Li
250			_aFirst edition 2021
264		1	_aCham, Switzerland _bSpringer International Publising _c2021
300			_a1 recurso en línea (XVIII, 404 páginas) _b163 ilustraciones, 130 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
505	0		_aPart I Privacy Preserving Queries -- Range Queries over Encrypted Data -- Fast and Scalable Range and Keyword Query Processing over Encrypted Data with Provable Adaptive Security -- Nearest Neighbor Queries over Encrypted Data -- K-nearest Neighbor Queries over Encrypted Data -- Top-k Queries for Two-tiered Sensor Networks -- Part II Privacy Preserving Computation -- Collaborative Enforcement of Firewall Policies in Virtual Private Networks -- Privacy Preserving Quantification of Cross-Domain Network Reachability -- Cross-Domain Privacy-Preserving Cooperative Firewall Optimization -- Privacy Preserving String Matching for Cloud Computing -- Privacy Preserving Information Hub Identification in Social Networks -- Part III Differential Privacy -- Publishing Social Network Data with Privacy Guarantees -- Predictable Privacy-Preserving Mobile Crowd Sensing -- Differentially Private and Budget Limited Bandit Learning over Matroids -- Part IV Breaking Privacy -- Breaching Privacy in Encrypted Instant Messaging Networks.
520	3		_aThis book introduces the state-of-the-art algorithms for data and computation privacy. It mainly focuses on searchable symmetric encryption algorithms and privacy preserving multi-party computation algorithms. This book also introduces algorithms for breaking privacy, and gives intuition on how to design algorithm to counter privacy attacks. Some well-designed differential privacy algorithms are also included in this book. Driven by lower cost, higher reliability, better performance, and faster deployment, data and computing services are increasingly outsourced to clouds. In this computing paradigm, one often has to store privacy sensitive data at parties, that cannot fully trust and perform privacy sensitive computation with parties that again cannot fully trust. For both scenarios, preserving data privacy and computation privacy is extremely important. After the Facebook-Cambridge Analytical data scandal and the implementation of the General Data Protection Regulation by European Union, users are becoming more privacy aware and more concerned with their privacy in this digital world. This book targets database engineers, cloud computing engineers and researchers working in this field. Advanced-level students studying computer science and electrical engineering will also find this book useful as a reference or secondary text.
988			_aSpringer_Computer_2021
650		7	_2embne _aSeguridad informática _9158200
650		7	_2embne _9249221 _aRedes informáticas _xMedidas de seguridad
700	1		_aLi, Rui _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678836
710	2		_aSpringerLink
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-58896-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2021 _dz _eb _zSI