<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Estimation of True Quantiles from Quantitative Data Obfuscated with Additive Noise

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Privacy protection and data security have recently received a substantial amount of attention due to the increasing need to protect various sensitive...

Estimation of True Quantiles from Quantitative Data Obfuscated with Additive Noise

Pubblicato online: 01 set 2018

Pagine: 671 - 694

Ricevuto: 01 mar 2016

Accettato: 01 ott 2017

DOI: https://doi.org/10.2478/jos-2018-0032

Parole chiave
Data obfuscation, quantile estimation, additive noise

© 2018 Debolina Ghatak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Estimation of True Quantiles from Quantitative Data Obfuscated with Additive Noise

Pubblicato online: 01 set 2018

Pagine: 671 - 694

Ricevuto: 01 mar 2016

Accettato: 01 ott 2017

DOI: https://doi.org/10.2478/jos-2018-0032

Parole chiaveData obfuscation, quantile estimation, additive noise

© 2018 Debolina Ghatak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Data obfuscation, quantile estimation, additive noise