Increasing Polynomial Regression Complexity for Data Anonymization

Autor:	Jordi Nin, Jordi Pont-Tuset, Pau Medrano-Gracia, Josep L. Larriba-Pey, and Victor Muntes-Mulero
Přispěvatelé:	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
Rok vydání:	2007
Předmět:	Protecció de dades Informàtica::Seguretat informàtica [Àrees temàtiques de la UPC] Ubiquitous computing Security of data Regression analysis Data protection
Zdroj:	UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Recercat. Dipósit de la Recerca de Catalunya instname
DOI:	10.1109/ipc.2007.103
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e2897e2515466deff6a3fa311de0a9f https://doi.org/10.1109/ipc.2007.103 Zobrazit plný text záznamu