Estimation of True Quantiles from Quantitative Data Obfuscated with Additive Noise

• Autores: Debolina Ghatak, Bimal Roy
• Localización: Journal of official statistics, ISSN 0282-423X, Vol. 34, Nº. 3, 2018 (Ejemplar dedicado a: Special Section on Responsive and Adaptive Survey Design), págs. 671-694
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • Privacy protection and data security have recently received a substantial amount of attention due to the increasing need to protect various sensitive information like credit card data and medical data. There are various ways to protect data. Here, we address ways that may as well retain its statistical uses to some extent. One such way is to mask a data with additive or multiplicative noise and revert to certain desired parameters of the original distribution from the knowledge of the noise distribution and masked data. In this article, we discuss the estimation of any desired quantile of a quantitative data set masked with additive noise. We also propose a method to choose appropriate parameters for the noise distribution and discuss advantages of this method over some existing methods.