论文标题
差异隐私变得容易
Differential Privacy Made Easy
论文作者
论文摘要
数据隐私是数十年来的主要问题，已经开发了几种技术来确保个人的隐私，但仍然存在隐私失败。 2006年，辛西娅·德沃（Cynthia Dwork）提出了差异隐私的想法，这为数据隐私提供了强大的理论保证。许多公司和研究机构都开发了不同的隐私库，但是为了获得差异化结果，用户必须调整隐私参数。在本文中，我们最大程度地减少了这些可曲的参数。开发了DP框架，该工程比较了三个基于Python的DP库的差异私有结果。我们还引入了一个新的非常简单的DP库（GRAM-DP），因此没有差异隐私背景的人们仍然可以确保数据集中个人的隐私，同时公开发布统计结果。
Data privacy is a major issue for many decades, several techniques have been developed to make sure individuals' privacy but still world has seen privacy failures. In 2006, Cynthia Dwork gave the idea of Differential Privacy which gave strong theoretical guarantees for data privacy. Many companies and research institutes developed differential privacy libraries, but in order to get the differentially private results, users have to tune the privacy parameters. In this paper, we minimized these tune-able parameters. The DP-framework is developed which compares the differentially private results of three Python based DP libraries. We also introduced a new very simple DP library (GRAM-DP), so the people with no background of differential privacy can still secure the privacy of the individuals in the dataset while releasing statistical results in public.
