Estimation of True Quantiles from Quantitative Data Obfuscated with Additive Noise
| Autor: | Bimal Roy, Debolina Ghatak |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
05 social sciences Statistics Data security Probability and statistics computer.software_genre 01 natural sciences Multiplicative noise HA1-4737 Data set 010104 statistics & probability Credit card Information sensitivity Noise 0502 economics and business Data mining 0101 mathematics computer data obfuscation quantile estimation additive noise 050205 econometrics Quantile |
| Zdroj: | Journal of Official Statistics, Vol 34, Iss 3, Pp 671-694 (2018) |
| ISSN: | 2001-7367 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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