Protection in Statistical Data Bases under Differential Privacy
The aim of this project is to create a masking methodology on financial data sets while ensuring the confidentiality of the data. The techniques to obtain sanitized data and test the accuracy for selected statistical analyses on masked data are determined. The possible attacks deciphering the proposed methodologies are studied and the ways to prevent them are targeted to be implemented into the privacy module. A user-friendly software, to generate a secure masked data set from the original data for the purposes of quantifying certain statistical methods is aimed to be prepared.
- A. Sevtap Selçuk-Kestel, Institute of Applied Mathematics, METU (Director)
- Murat Cenk, Institute of Applied Mathematics, METU (Researcher)
- Ersan Akyıldız, Institute of Applied Mathematics, METU (Consultant)
Funded by Central Bank of the Republic of Turkey, October 28 2016 - February 27 2019.