BUDS: Balancing Utility and Differential Privacy by Shuffling
Abstract
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 $\epsilon = 0.02$ which is a very promising result. Our algorithm maintains a privacy bound of $\epsilon = ln [t/((n_1 - 1)^S)]$ and loss bound of $c' \bigg|e^{ln[t/((n_1 - 1)^S)]} - 1\bigg|$.
- Publication:
-
arXiv e-prints
- Pub Date:
- June 2020
- DOI:
- 10.48550/arXiv.2006.04125
- arXiv:
- arXiv:2006.04125
- Bibcode:
- 2020arXiv200604125S
- Keywords:
-
- Computer Science - Machine Learning;
- Computer Science - Cryptography and Security;
- Statistics - Machine Learning
- E-Print:
- 11 pages, 3 images, 3 tables, Accepted to 11th ICCCNT, 2020, IIT KGP