Information Driven Evaluation of Data Hiding Algorithms

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Author

Elisa Bertino and Igor Nai Fovino

Tech report number

CERIAS TR 2005-108

Entry type

proceedings

Abstract

s are used. Privacy Preserving Data Mining (PPDM) algorithms have been recently introduced with the aim of mod- ifying the database in such a way to prevent the discovery of sensible information. Due to the large amount of possible techniques that can be used to achieve this goal, it is necessary to provide some standard evalu- ation metrics to determine the best algorithms for a specific application or context. Currently, however, there is no common set of parameters that can be used for this purpose. This paper explores the problem of PPDM algorithm evaluation, starting from the key goal of preserving of data quality. To achieve such goal, we propose a formal definition of data quality specifically tailored for use in the context of PPDM algorithms, a set of evaluation parameters and an evaluation algorithm. The resulting evaluation core process is then presented as a part of a more general three step evaluation framework, taking also into account other aspects of the algorithm evaluation such as efficiency, scalability and level of privacy.

Date

2005

Key alpha

Bertino

Publisher

Springer-Verlag Berlin Heidelberg 2005

Publication Date

2005-01-01

Copyright

Springer-Verlag Berlin Heidelberg 2005

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@Proceedings{ Bertino,
	title = "Information Driven Evaluation of Data Hiding Algorithms ",
	author = "Elisa Bertino and Igor Nai Fovino",
	year = "2005",
	publisher = "Springer-Verlag Berlin Heidelberg 2005",
	abstract = "s are used. Privacy Preserving Data Mining 
(PPDM) algorithms have been recently introduced with the aim of mod- 
ifying the database in such a way to prevent the discovery of sensible 
information. Due to the large amount of possible techniques that can be 
used to achieve this goal, it is necessary to provide some standard evalu- 
ation metrics to determine the best algorithms for a specific application 
or context. Currently, however, there is no common set of parameters 
that can be used for this purpose. This paper explores the problem of 
PPDM algorithm evaluation, starting from the key goal of preserving of 
data quality. To achieve such goal, we propose a formal definition of data 
quality specifically tailored for use in the context of PPDM algorithms, a 
set of evaluation parameters and an evaluation algorithm. The resulting 
evaluation core process is then presented as a part of a more general three 
step evaluation framework, taking also into account other aspects of the 
algorithm evaluation such as efficiency, scalability and level of privacy. ",
	copyright = "Springer-Verlag Berlin Heidelberg 2005",
}