Increasing polynomial regression complexity for data anonymization
Document typeConference report
PublisherIEEE Computer Society
Rights accessOpen Access
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.
CitationNin, J. [et al.]. Increasing polynomial regression complexity for data anonymization. A: International Conference on Intelligent Pervasive Computing. "2007 International Conference on Intelligent Pervasive Computing". Jeju Island: IEEE Computer Society, 2007, p. 29-34.
- DAMA-UPC - Data Management Group de la Universitat Politècnica de Catalunya - Ponències/Comunicacions de congressos 
- Departament d'Arquitectura de Computadors - Ponències/Comunicacions de congressos [1.722]
- Departament de Teoria del Senyal i Comunicacions - Ponències/Comunicacions de congressos [3.127]
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