Estimation of True Quantiles from Quantitative Data Obfuscated with Additive Noise

Ghatak Debolina () and Roy Bimal ()
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Ghatak Debolina: Indian Statistical Institute, Applied Statistics Unit, p. 8. Basudebpur Sarsuna Main Road, Kolkata700108, India.
Roy Bimal: Indian Statistical Institute, Applied Statistics Unit, p. 8. Basudebpur Sarsuna Main Road, Kolkata700108, India.

Journal of Official Statistics, 2018, vol. 34, issue 3, 671-694

Abstract: 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.

Keywords: Data obfuscation; quantile estimation; additive noise (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:34:y:2018:i:3:p:671-694:n:5

DOI: 10.2478/jos-2018-0032

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