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Data Privacy Games (Hardcover, 1st ed. 2018)
Loot Price: R3,402
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Data Privacy Games (Hardcover, 1st ed. 2018)
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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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