  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 $c' \bigg|e^{ln[t/((n_1 - 1)^S)]} -
1\bigg|$.
