# BUDS: Balancing Utility and Differential Privacy by Shuffling

arXiv: https://arxiv.org/abs/2006.04125 · HTML: https://mlsanity.com/paper/2006.04125/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Privacy-Utility-Fairness: A Balanced Approach to Vehicular-Traffic Management System](https://mlsanity.com/paper/2507.08864/) (2025)

## Abstract (arXiv)

Balancing utility and differential privacy by shuffling or textitBUDS 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' bigg|eln[t/((n1 - 1)S)] - 1bigg|.