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Descripción

Hypothesis testing is a useful statistical tool in determining whether a given model should be rejected based on a sample from the population. Sample data may contain sensitive information about individuals, such as medical information. Thus it is important to design statistical tests that guarantee the privacy of subjects in the data. In this work, we study hypothesis testing subject to differential privacy, specifically chi-squared tests for goodness of fit for multinomial data and independence between two categorical variables.

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Autor(es)

Gaboardi, Marco -  Lim, Hyun -  Woo -  Rogers, Ryan M. -  Vadhan, Salil P. - 

Id.: 70848338

Idioma: inglés (Estados Unidos)  - 

Versión: 1.0

Estado: Final

Tipo de recurso: Conference Paper  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

: open

Requerimientos técnicos:  Browser: Any - 

Relación: [References] https://arxiv.org/abs/1602.03090
[References] ICML'16 Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48

Fecha de contribución: 11-ene-2018

Contacto:

Localización:
* Gaboardi, Marco, Hyun-Woo Lim, Ryan M. Rogers, and Salil P. Vadhan. 2016. "Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing." In ICML'16 Proceedings of the 33rd International Conference on International Conference on Machine Learning, New York, NY, June 19-24, 2016, Volume 48: 2111-2120.

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