International Association
for Cryptologic Research

Differential privacy (DP) has emerged as a preferred solution for privacy-preserving data analysis, having been adopted by several leading Internet companies. DP is a privacy-preserving mechanism that protects against re-identification of individuals within aggregated datasets. It is known that the privacy budget $\varepsilon$ determines the trade-off between privacy and utility. In this paper, we propose the use of novel set of metrics and an easy-to-implement, step-by-step framework to facilitate the implementation of the DP mechanism on real-world datasets and guide the selection of $\varepsilon$ based on desired accuracy vs utility trade-off. Currently, for a given query there is no widely accepted methodology on how to select $\varepsilon$ and choose the best DP mechanism that offers an optimal trade-off between privacy and utility. In order to address this gap, we perform experiments by considering three real-world datasets, aiming to identify optimal $\varepsilon$ and suitable mechanisms (Laplace or Gaussian) based on privacy utility trade-off as per use case for the commonly used count, sum and average queries for each dataset. Based on our experiment results, we observe that using our metric and framework, one can analyse noise distribution charts of multiple queries, and choose the suitable $\varepsilon$ and the DP mechanism for achieving a balance between privacy and utility. Additionally, we show that the optimal $\varepsilon$ depends on the particular query, desired accuracy and context in which DP is implemented, which suggests that an arbitrary, a-prior selection of $\varepsilon$ cannot provide adequate results. Our framework prioritises the plotting and visualisation of values and results in the DP analysis, making its adoption easy for a wider audience.