Title:
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Increasing polynomial regression complexity for data anonymization
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Author:
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Nin Guerrero, Jordi; Pont Tuset, Jordi; Medrano Gracia, Pau; Larriba Pey, Josep; Muntés Mulero, Víctor
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Other authors:
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Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors; Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. DAMA-UPC - Data Management Group |
Abstract:
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Pervasive computing and the increasing networking needs usually demand from publishing data without revealing sensible
information. Among several data protection methods proposed in the literature, those based on linear regression are widely used for numerical data. However, no attempts
have been made to study the effect of using more complex polynomial regression methods. In this paper, we present PoROP-k, a family of anonymizing methods able to protect a
data set using polynomial regressions. We show that PoROP-k not only reduces the loss of information, but it also obtains a better level of protection compared to previous proposals based on linear regressions. |
Abstract:
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Peer Reviewed |
Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Seguretat informàtica -Data protection -Regression analysis -Security of data -Ubiquitous computing -Protecció de dades |
Rights:
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Document type:
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Article - Published version Conference Object |
Published by:
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IEEE Computer Society
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