國立高雄大學圖資館 |

Language: English

Back

Data privacy games

SpringerLink (Online service)

Data privacy games

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data privacy gamesby Lei Xu ... [et al.].
other author:	Xu, Lei.
Published:	Cham :Springer International Publishing :2018.
Description:	x, 181 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Computer science.
Online resource:	http://dx.doi.org/10.1007/978-3-319-77965-2
ISBN:	9783319779652$q(electronic bk.)

Data privacy games
Data privacy games[electronic resource] /by Lei Xu ... [et al.]. - Cham :Springer International Publishing :2018. - x, 181 p. :ill., digital ;24 cm.

1 The Conflict between Big Data and Individual Privacy -- 2 Privacy-Preserving Data Collecting: A Simple Game Theoretic Approach -- 3 Contract-based Private Data Collecting -- 4 Dynamic Privacy Pricing -- 5 User Participation Game in Collaborative Filtering -- 6 Privacy-Accuracy Trade-off in Distributed Data Mining -- 7 Conclusion.

With the growing popularity of "big data", the potential value of personal data has attracted more and more attention. Applications built on personal data can create tremendous social and economic benefits. Meanwhile, they bring serious threats to individual privacy. The extensive collection, analysis and transaction of personal data make it difficult for an individual to keep the privacy safe. People now show more concerns about privacy than ever before. How to make a balance between the exploitation of personal information and the protection of individual privacy has become an urgent issue. In this book, the authors use methodologies from economics, especially game theory, to investigate solutions to the balance issue. They investigate the strategies of stakeholders involved in the use of personal data, and try to find the equilibrium. The book proposes a user-role based methodology to investigate the privacy issues in data mining, identifying four different types of users, i.e. four user roles, involved in data mining applications. For each user role, the authors discuss its privacy concerns and the strategies that it can adopt to solve the privacy problems. The book also proposes a simple game model to analyze the interactions among data provider, data collector and data miner. By solving the equilibria of the proposed game, readers can get useful guidance on how to deal with the trade-off between privacy and data utility. Moreover, to elaborate the analysis on data collector's strategies, the authors propose a contract model and a multi-armed bandit model respectively. The authors discuss how the owners of data (e.g. an individual or a data miner) deal with the trade-off between privacy and utility in data mining. Specifically, they study users' strategies in collaborative filtering based recommendation system and distributed classification system. They built game models to formulate the interactions among data owners, and propose learning algorithms to find the equilibria.

ISBN: 9783319779652$q(electronic bk.)

Standard No.: 10.1007/978-3-319-77965-2doiSubjects--Topical Terms:

199325
Computer science.

LC Class. No.: QA76.9.D35

Dewey Class. No.: 005.74

Data privacy games
LDR:03271nmm a2200325 a 4500 001 537670
003 DE-He213
005 20180424145855.0
006 m d
007 cr nn 008maaau
008 190116s2018 gw s 0 eng d
020 $a 9783319779652$q(electronic bk.)
020 $a 9783319779645$q(paper)
024 7 $a 10.1007/978-3-319-77965-2 $2 doi
035 $a 978-3-319-77965-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D35
072 7 $a UMB $2 bicssc
072 7 $a URY $2 bicssc
072 7 $a COM031000 $2 bisacsh
082 0 4 $a 005.74 $2 23
090 $a QA76.9.D35 $b D232 2018
245 0 0 $a Data privacy games $h [electronic resource] / $c by Lei Xu ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a x, 181 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1 The Conflict between Big Data and Individual Privacy -- 2 Privacy-Preserving Data Collecting: A Simple Game Theoretic Approach -- 3 Contract-based Private Data Collecting -- 4 Dynamic Privacy Pricing -- 5 User Participation Game in Collaborative Filtering -- 6 Privacy-Accuracy Trade-off in Distributed Data Mining -- 7 Conclusion.
520 $a With the growing popularity of "big data", the potential value of personal data has attracted more and more attention. Applications built on personal data can create tremendous social and economic benefits. Meanwhile, they bring serious threats to individual privacy. The extensive collection, analysis and transaction of personal data make it difficult for an individual to keep the privacy safe. People now show more concerns about privacy than ever before. How to make a balance between the exploitation of personal information and the protection of individual privacy has become an urgent issue. In this book, the authors use methodologies from economics, especially game theory, to investigate solutions to the balance issue. They investigate the strategies of stakeholders involved in the use of personal data, and try to find the equilibrium. The book proposes a user-role based methodology to investigate the privacy issues in data mining, identifying four different types of users, i.e. four user roles, involved in data mining applications. For each user role, the authors discuss its privacy concerns and the strategies that it can adopt to solve the privacy problems. The book also proposes a simple game model to analyze the interactions among data provider, data collector and data miner. By solving the equilibria of the proposed game, readers can get useful guidance on how to deal with the trade-off between privacy and data utility. Moreover, to elaborate the analysis on data collector's strategies, the authors propose a contract model and a multi-armed bandit model respectively. The authors discuss how the owners of data (e.g. an individual or a data miner) deal with the trade-off between privacy and utility in data mining. Specifically, they study users' strategies in collaborative filtering based recommendation system and distributed classification system. They built game models to formulate the interactions among data owners, and propose learning algorithms to find the equilibria.
650 0 $a Computer science. $3 199325
650 0 $a Data structures (Computer science) $3 183917
650 0 $a Data mining. $3 184440
650 0 $a Information storage and retrieval. $3 731230
650 0 $a Management information systems. $3 199355
650 0 $a Electronic commerce. $3 189326
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Data Structures, Cryptology and Information Theory. $3 273993
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Information Storage and Retrieval. $3 274190
650 2 4 $a Management of Computing and Information Systems. $3 274191
650 2 4 $a e-Commerce/e-business. $3 348145
700 1 $a Xu, Lei. $3 260590
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-77965-2
950 $a Computer Science (Springer-11645)