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Data privacy games

Xu, Lei.

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Data privacy games

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data privacy games/ by Lei Xu ... [et al.].
other author:	Xu, Lei.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	x, 181 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Computer science. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-77965-2
ISBN:	9783319779652

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

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

523869
Computer science.

LC Class. No.: QA76.9.D35

Dewey Class. No.: 005.74

Data privacy games
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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.
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650 0 $a Data structures (Computer science) $3 527210
650 0 $a Data mining. $3 562972
650 0 $a Information storage and retrieval. $3 736119
650 0 $a Management information systems. $3 528143
650 0 $a Electronic commerce. $3 576153
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650 2 4 $a Data Structures, Cryptology and Information Theory. $3 891008
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
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650 2 4 $a Management of Computing and Information Systems. $3 892490
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