BUDS: Balancing Utility and Differential Privacy by Shuffling
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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 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' |e^ln[t/((n_1 - 1)^S)] - 1|.
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