| Title | BUDS: Balancing Utility and Differential Privacy by Shuffling |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Sengupta, Poushali, Paul, Sudipta, Mishra, Subhankar |
| Conference Name | 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) |
| Date Published | jul |
| Keywords | Computing Theory, Computing Theory and Privacy, Databases, Differential privacy, Differential privacy(DP), encoding, Noise measurement, privacy, Protocols, pubcrawl, Resiliency, risk management, Shuffling, utility |
| 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 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. |
| DOI | 10.1109/ICCCNT49239.2020.9225470 |
| Citation Key | sengupta_buds_2020 |
BUDS: Balancing Utility and Differential Privacy by Shuffling