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dc.contributor.authorNAI FOVINO IGORen_GB
dc.contributor.authorMASERA MARCELOen_GB
dc.date.accessioned2010-02-25T15:40:41Z-
dc.date.available2008-01-30en_GB
dc.date.available2010-02-25T15:40:41Z-
dc.date.created2008-01-22en_GB
dc.date.issued2008en_GB
dc.date.submitted2008-01-15en_GB
dc.identifier.issn1018-5593en_GB
dc.identifier.otherEUR 23069 ENen_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC42699-
dc.description.abstractPrivacy is one of the most important properties an information system must satisfy. A relatively new trend shows that classical access control techniques are not sufficient to guarantee privacy when datamining techniques are used. Privacy Preserving Data Mining (PPDM) algorithms have been recently introduced with the aim of modifying 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 evaluation 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. Moreover, because sanitization modifies the data, an important issue, especially for critical data, is to preserve the quality of data. However, to the best of our knowledge, no approaches have been developed dealing with the issue of data quality in the context of PPDM algorithms. This report 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. Moreover, because of the "environment related" nature of data quality, a structure to represent constraints and information relevance related to data is presented. 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.en_GB
dc.description.sponsorshipJRC.G.6-Sensors, radar technologies and cybersecurityen_GB
dc.format.mediumPrinteden_GB
dc.languageENGen_GB
dc.publisherOPOCEen_GB
dc.relation.ispartofseriesJRC42699en_GB
dc.titlePrivacy Preserving Data Mining, Evaluation Methodologiesen_GB
dc.typeEUR - Scientific and Technical Research Reportsen_GB
JRC Directorate:Space, Security and Migration

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