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对差异性隐私开源工具的评估

Shiliang Zhang^1,2, Anton Hagermalm¹, Sanjin Slavnic¹

¹Computer Science and Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden.

Sensors (Basel, Switzerland)

|July 29, 2023

概括

本研究介绍了差异性隐私 (DP) 工具的评估框架,比较它们的隐私-实用性权衡和资源使用. 结果引导开发人员为各种应用选择最佳的DP解决方案.

科学领域:

计算机科学计算机科学
数据隐私数据隐私数据
软件工程软件工程软件工程

背景情况:

差异隐私 (DP) 通过向数据添加噪音来提供可量化的隐私保护,从而创建隐私-实用性权衡.
有许多开源工具用于DP实现,但缺乏全面的比较.
评估这些工具的隐私-实用平衡和系统资源使用对于实际采用至关重要.

研究的目的:

提出并利用一个开源的评估框架来比较不同的隐私工具.
评估领先的DP工具的性能,包括OpenDP Smartnoise,谷歌DP,PyTorch Opacus,Tensorflow Privacy和Diffprivlib. 这三种工具.
分析不同数据大小和属性类型 (离散/连续) 的隐私-实用性权衡和系统资源消耗.

主要方法:

开发一个对隐私保护解决方案的开源评估框架.
实证评估五个著名的开源DP工具.
性能指标的量化,包括隐私-实用平衡和系统资源使用.
在不同的数据大小和离散和连续数据属性的测试.

主要成果:

该研究揭示了评估的DP工具中不同的性能模式.
工具如何平衡隐私保护与数据实用性的方法存在显著差异.
资源消耗 (例如,内存,计算时间) 在被调查的工具中显著不同.
性能受数据特征的影响,例如大小和属性类型 (离散与连续).

结论:

这些发现为开发人员选择基于特定应用程序要求的DP工具提供了实用见解.
这种评估是做出明智选择和更快地采用差异性隐私技术的基础.
了解工具特定的权衡对于有效和高效的保护隐私的数据分析至关重要.

关键词:

不同的隐私差异隐私差异评价评价评价评价开源工具是开源工具.

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