Subset Privacy：从混淆的骨灰盒中提取
Subset Privacy: Draw from an Obfuscated Urn
摘要：
随着收集和分析个人数据的能力的迅速提高，数据隐私成为一个新兴的问题。在这项工作中，我们开发了一个新的本地隐私的统计概念，以保护将由不可信的实体收集的每个分类数据。所提出的解决方案称为子集隐私，通过用包含该值的随机子集替换原始数据值来将其私有化。我们开发的方法估计的分布函数和独立性测试的子集私有数据的理论保证。我们还研究了不同的机制来实现子集隐私和评估指标，以量化在实践中的隐私量。模拟和真实世界的数据集上的实验结果表明，令人鼓舞的性能的概念和方法。
英文摘要：
With the rapidly increasing ability to collect and analyze personal data, data privacy becomes an emerging concern. In this work, we develop a new statistical notion of local privacy to protect each categorical data that will be collected by untrusted entities. The proposed solution, named subset privacy, privatizes the original data value by replacing it with a random subset containing that value. We develop methods for the estimation of distribution functions and independence testing from subset-private data with theoretical guarantees. We also study different mechanisms to realize the subset privacy and evaluation metrics to quantify the amount of privacy in practice. Experimental results on both simulated and real-world datasets demonstrate the encouraging performance of the developed concepts and methods.
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