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Books > Computing & IT > Computer communications & networking > Network security
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Differential Privacy - From Theory to Practice (Paperback)
Loot Price: R1,412
Discovery Miles 14 120
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Differential Privacy - From Theory to Practice (Paperback)
Series: Synthesis Lectures on Information Security, Privacy, and Trust
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Over 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.
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