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/((n1−1)S)] and loss bound of c′eln[t/((n1−1)S)]−1.
@article{arxiv.2006.04125,
title = {BUDS: Balancing Utility and Differential Privacy by Shuffling},
author = {Poushali Sengupta and Sudipta Paul and Subhankar Mishra},
journal= {arXiv preprint arXiv:2006.04125},
year = {2020}
}