ABSTRACT:
The increasing interest in collecting and publishing large amounts of
individuals’ data to public for purposes such as medical research, market
analysis and economical measures has created major privacy concerns about
individual’s sensitive information. To deal with these concerns, many
Privacy-Preserving Data Publishing (PPDP) techniques have been proposed in
literature. However, they lack a proper privacy characterization and
measurement. In this paper, we first present a novel multi-variable privacy characterization
and quantification model. Based on this model, we are able to analyze the prior
and posterior adversarial belief about attribute values of individuals. We can
also analyze the sensitivity of any identifier in privacy characterization.
Then we show that privacy should not be measured based on one metric. We
demonstrate how this could result in privacy misjudgment.
We propose two different metrics for quantification of privacy leakage,
distribution leakage and entropy leakage. Using these metrics, we analyzed some
of the most well-known PPDP techniques such as k-anonymity, l-diversity and
t-closeness. Based on our framework and the proposed metrics, we can determine
that all the existing PPDP schemes have limitations in privacy
characterization. Our proposed privacy characterization and measurement
framework contributes to better understanding and evaluation of these
techniques. Thus, this paper provides a foundation for design and analysis of
PPDP schemes
SYSTEM
REQUIREMENTS:
HARDWARE
REQUIREMENTS:
·
System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor :
15’’ LED
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Input
Devices : Keyboard, Mouse
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Ram : 1
GB
SOFTWARE
REQUIREMENTS:
·
Operating
system : Windows 7.
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Coding
Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database
: MYSQL
REFERENCE:
M.H. Afifi,
Kai Zhou and Jian Ren,
“Privacy Characterization and Quantification in Data Publishing”, IEEE
Transactions on Knowledge and Data Engineering, 2018.