A Framework for Evaluating Privacy Preserving Data Mining Algorithms

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Author

ELISA BERTINO, IGOR NAI FOVINO , LOREDANA PARASILITI PROVENZA

Tech report number

CERIAS TR 2005-96

Entry type

article

Abstract

Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive information. Several data mining techniques, incorporating privacy protection mechanisms, have been developed that allow one to hide sensitive itemsets or patterns, before the data mining process is executed. Privacy preserving classification methods, instead, prevent a miner from building a classifier which is able to predict sensitive data. Additionally, privacy preserving clustering techniques have been recently proposed, which distort sensitive numerical attributes, while preserving general features for clustering analysis. A crucial issue is to determine which ones among these privacy-preserving techniques better protect sensitive information. However, this is not the only criteria with respect to which these algorithms can be evaluated. It is also important to assess the quality of the data resulting from the modifications applied by each algorithm, as well as the performance of the algorithms. There is thus the need of identifying a comprehensive set of criteria with respect to which to assess the existing PPDM algorithms and determine which algorithm meets specific requirements. In this paper, we present a first evaluation framework for estimating and comparing different kinds of PPDM algorithms. Then, we apply our criteria to a specific set of algorithms and discuss the evaluation results we obtain. Finally, some considerations about future work and promising directions in the context of privacy preservation in data mining are discussed.

Date

2005 – 18 – 08

Booktitle

Data Mining and Knowledge Discovery, 11

Key alpha

A Framework for Evaluating Privacy Preserving Data Mining Algorithms

Organization

EU under the IST Project CODMINE and by the Sponsors of CERIAS.

Publisher

Springer Science+Business Media, Inc.

School

CERIAS and CS Department, Purdue University, and Dipartimento di Informatica e Comunicazione, Universit `a degli Studi di Milano

Publication Date

2001-01-01

Copyright

2005 Springer Science+Business Media, Inc.

BibTex-formatted data

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@Article{ A Framework for Evaluating Privacy Preserving Data Mining Algorithms,
	title = "A Framework for Evaluating Privacy Preserving Data Mining Algorithms",
	author = "ELISA BERTINO, IGOR NAI FOVINO , LOREDANA PARASILITI PROVENZA ",
	year = "2005",
	month = "18",
	booktitle = "Data Mining and Knowledge Discovery, 11",
	organization = " EU under the IST Project CODMINE and by the Sponsors of CERIAS.",
	publisher = "Springer Science+Business Media, Inc.",
	school = "CERIAS and CS Department, Purdue University, and Dipartimento di Informatica e Comunicazione, Universit `a degli Studi di Milano ",
	day = "08",
	abstract = "Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) 
algorithms, has been developed by the research community working on security and knowledge discovery. The aim 
of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same 
time sensitive information. Several data mining techniques, incorporating privacy protection mechanisms, have 
been developed that allow one to hide sensitive itemsets or patterns, before the data mining process is executed. 
Privacy preserving classification methods, instead, prevent a miner from building a classifier which is able to 
predict sensitive data. Additionally, privacy preserving clustering techniques have been recently proposed, which 
distort sensitive numerical attributes, while preserving general features for clustering analysis. A crucial issue is to 
determine which ones among these privacy-preserving techniques better protect sensitive information. However, 
this is not the only criteria with respect to which these algorithms can be evaluated. It is also important to assess 
the quality of the data resulting from the modifications applied by each algorithm, as well as the performance 
of the algorithms. There is thus the need of identifying a comprehensive set of criteria with respect to which to 
assess the existing PPDM algorithms and determine which algorithm meets specific requirements. 
In this paper, we present a first evaluation framework for estimating and comparing different kinds of PPDM 
algorithms. Then, we apply our criteria to a specific set of algorithms and discuss the evaluation results we obtain. 
Finally, some considerations about future work and promising directions in the context of privacy preservation in data mining are discussed. ",
	copyright = " 2005 Springer Science+Business Media, Inc.",
}