BUDS: balancing utility and differential privacy by shuffling Authors

Poushali Sengupta, sudipta paul, Subhankar Mishra

Abstract: Balancing utility and differential privacy by shuffling or 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 the empirical tests 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 ' |e ln[t/((n1-1)S )]-1|.