Visible to the public BUDS: Balancing Utility and Differential Privacy by Shuffling

TitleBUDS: Balancing Utility and Differential Privacy by Shuffling
Publication TypeConference Paper
Year of Publication2020
AuthorsSengupta, Poushali, Paul, Sudipta, Mishra, Subhankar
Conference Name2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)
Date Publishedjul
KeywordsComputing Theory, Computing Theory and Privacy, Databases, Differential privacy, Differential privacy(DP), encoding, Noise measurement, privacy, Protocols, pubcrawl, Resiliency, risk management, Shuffling, utility
AbstractBalancing 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/((n1-1)S)] and loss bound of c'\textbackslashtextbareln[t/((n1-1)S)]-1\textbackslashtextbar.
Citation Keysengupta_buds_2020