An increasing number of open-source libraries promise to bring differential privacy to practice, even for non-experts. This paper studies five libraries that offer differentially private analytics: Google DP, SmartNoise, diffprivlib, diffpriv, and Chorus. We compare these libraries qualitatively (capabilities, features, and maturity) and quantitatively (utility and scalability) across four analytics queries (count, sum, mean, and variance) executed on synthetic and real-world datasets. We conclude that these libraries provide similar utility (except in some notable scenarios). However, there are significant differences in the features provided, and we find that no single library excels in all areas. Based on our results, we provide guidance for practitioners to help in choosing a suitable library, guidance for library designers to enhance their software, and guidance for researchers on open challenges in differential privacy tools for non-experts.
翻译:越来越多的开放源码图书馆承诺将不同的隐私(即使是非专家的隐私)付诸实践。本文研究了五家提供不同程度私人分析的图书馆:谷歌DP、SmartNoise、diffprivlib、diffpriv、diffpriv和Chorus。我们将这些图书馆的质量(能力、特征和成熟度)和数量(功用和可扩展性)与合成和真实世界数据集的四种分析查询(计算、总和、平均值和差异)进行比较。我们的结论是,这些图书馆提供了相似的用途(某些显著的假想除外 ) 。 然而,所提供的功能差异很大,我们发现在所有领域都没有单一的图书馆。根据我们的成果,我们为从业人员提供指导,帮助选择合适的图书馆,为图书馆设计者提供指导,以加强软件,并为研究人员提供关于非专家不同隐私工具公开挑战的指导。