数据刷新。

Xiaotong Shen, Xuan Bi, Rex Shen
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引用次数: 2

摘要

数据扰动是一种通过在原始数据中添加“噪声”来生成合成数据的技术,在科学和工程中有一系列应用,主要是在数据安全和隐私方面。数据扰动的一个挑战是,它通常会产生合成数据,从而以牺牲隐私保护为代价导致信息丢失。而信息的丢失,又会导致任何基于合成数据的统计或机器学习方法的准确性下降,削弱下游分析能力,机器学习能力下降。在本文中,我们介绍并提倡数据摄动的基本原理,它要求保留原始数据的分布。为了实现这一目标,我们提出了一种名为数据刷新的新方案,该方案确定了下游分析的有效性,并保持了学习任务的预测准确性。它在满足严格的隐私保护要求(如差分隐私)的同时,对数据进行非线性扰动。我们通过示例强调数据刷新的多个方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Data Flush.

Data perturbation is a technique for generating synthetic data by adding "noise" to raw data, which has an array of applications in science and engineering, primarily in data security and privacy. One challenge for data perturbation is that it usually produces synthetic data resulting in information loss at the expense of privacy protection. The information loss, in turn, renders the accuracy loss for any statistical or machine learning method based on the synthetic data, weakening downstream analysis and deteriorating in machine learning. In this article, we introduce and advocate a fundamental principle of data perturbation, which requires the preservation of the distribution of raw data. To achieve this, we propose a new scheme, named data flush, which ascertains the validity of the downstream analysis and maintains the predictive accuracy of a learning task. It perturbs data nonlinearly while accommodating the requirement of strict privacy protection, for instance, differential privacy. We highlight multiple facets of data flush through examples.

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