BUDS: Balancing Utility and Differential Privacy by Shuffling
This work addresses privacy-utility trade-offs in statistical databases for data analysts, but it appears incremental as it builds on existing differential privacy theory with a novel shuffling technique.
The paper tackles the problem of balancing utility and differential privacy in crowd-sourced statistical databases by proposing BUDS, an algorithm that uses one-hot encoding and iterative shuffling with loss estimation and risk minimization, achieving a promising differential privacy parameter of ε=0.02 in empirical tests.
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 $ε= 0.02$ which is a very promising result. Our algorithm maintains a privacy bound of $ε= ln [t/((n_1 - 1)^S)]$ and loss bound of $c' \bigg|e^{ln[t/((n_1 - 1)^S)]} - 1\bigg|$.