Optimal Gamma density to Obfuscate Quantitative data with Added Noise

TL;DR

This paper proposes an optimal Gamma distribution-based noise addition method to protect individual privacy in quantitative data while maximizing data utility.

Contribution

It introduces a novel approach for selecting an optimal Gamma density for noise addition to enhance privacy without sacrificing data usefulness.

Findings

Identifies the Gamma distribution as suitable for data obfuscation.

Provides a method to select the optimal Gamma parameters for privacy.

Balances privacy protection with data utility effectively.

Abstract

Protecting the privacy of individuals in a data-set is no less important than making statistical inferences from it. In case the data in hand is quantitative, the usual way to protect it is to add a noise to the individual data values. But, what should be an ideal density used to generate the noise, so that we can get the maximum use of the data, without compromising privacy? In this paper, we deal with this problem and propose a method of selecting a density within the Gamma family that is optimal for this purpose.