BUDS: Balancing Utility and Differential Privacy by Shuffling

TL;DR

BUDS introduces a novel shuffling algorithm using one-hot encoding and iterative shuffling to achieve a strong balance between data utility and differential privacy in crowd-sourced databases.

Contribution

The paper proposes a new algorithm combining attribute shuffling, loss estimation, and risk minimization to enhance privacy-utility trade-offs in differential privacy.

Findings

Achieves a privacy level of ε=0.02 in empirical tests.

Maintains a privacy bound of ln[t/((n_1 - 1)^S)].

Provides a loss bound related to the privacy parameters.

Abstract

Balancing utility and differential privacy by shuffling or \textit{BUDS} is an approach towards crowd-sourced, statistical databases, with strong privacy and utility balance using differential privacy theory. Here, a novel algorithm is proposed using one-hot encoding and iterative shuffling with the loss estimation and risk minimization techniques, to balance both the utility and privacy. In this work, after collecting one-hot encoded data from different sources and clients, a step of novel attribute shuffling technique using iterative shuffling (based on the query asked by the analyst) and loss estimation with an updation function and risk minimization produces a utility and privacy balanced differential private report. During empirical test of balanced utility and privacy, BUDS produces $\epsilon = 0.02$ which is a very promising result. Our algorithm maintains a privacy bound of $\epsilon = ln [t/((n_1 - 1)^S)]$ and loss bound of $c' \bigg|e^{ln[t/((n_1 - 1)^S)]} - 1\bigg|$.