论文标题
芽：平衡效用和差异隐私通过改组
BUDS: Balancing Utility and Differential Privacy by Shuffling
论文作者
论文摘要
通过改组或\ textit {buds}平衡效用和差异隐私是一种使用差异隐私理论，具有强大的隐私和效用平衡的方法。在这里，提出了一种新颖的算法，并使用单热编码和迭代的改组与损失估计和风险最小化技术，以平衡效用和隐私。在这项工作中，在收集了来自不同来源和客户的单热编码数据之后，使用迭代改组的新型属性洗牌技术的一步（基于分析师要求的查询）和具有更新功能的损失估算和风险最小化的损失估算可产生实用性和隐私性平衡的不同私人私人报告。在对均衡效用和隐私的经验测试期间，芽产生$ε= 0.02 $，这是一个非常有希望的结果。我们的算法保持$ε= ln [t/（（n_1-1）^s）] $和$ c'\ bigG | e^{ln [t/（n_1-1-1-1）} -1 \ bigg | $的隐私限制。
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 $ε= 0.02$ which is a very promising result. Our algorithm maintains a privacy bound of $ε= ln [t/((n_1 - 1)^S)]$ and loss bound of $c' \bigg|e^{ln[t/((n_1 - 1)^S)]} - 1\bigg|$.
