A Programming Framework for Differential Privacy with Accuracy Concentration Bounds
A Programming Framework for Differential Privacy with Accuracy Concentration Bounds
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DOI：
10.1109/sp40000.2020.00086
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发表时间：
2019-09
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影响因子：
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通讯作者：
Elisabet Lobo Vesga;Alejandro Russo;Marco Gaboardi
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文献类型：
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作者：
Elisabet Lobo Vesga;Alejandro Russo;Marco Gaboardi
差分隐私提供了一个正式的框架，用于推理隐私和隐私数据计算的准确性。它还为构建私有数据分析提供了丰富的构建块。经过仔细校准后，这些分析同时保证了贡献数据的个人的隐私，以及数据分析结果的准确性，从而推断出有关人口的有用属性。差分隐私的组成性质已经激发了几种编程语言的设计和实现，旨在帮助数据分析师编程差分隐私分析。然而，迄今为止提出的大多数差分隐私编程语言都支持对隐私的推理，但不支持对数据分析准确性的推理。为了克服这一局限性，在这项工作中，我们提出了DPella，一个编程框架，为数据分析师提供支持，用于推理隐私，准确性及其权衡。DPella的显著特点是一个新的组件，可以静态跟踪不同数据分析的准确性。为了进行更严格的准确性估计，该组件利用污点分析来自动推断为保证隐私而添加的不同噪声量的统计独立性。我们通过实施文献中的几个经典查询来评估我们的方法，并展示数据分析师如何找出校准隐私以满足准确性要求的最佳方式。
Differential privacy offers a formal framework for reasoning about privacy and accuracy of computations on private data. It also offers a rich set of building blocks for constructing private data analyses. When carefully calibrated, these analyses simultaneously guarantee the privacy of the individuals contributing their data, and the accuracy of the data analyses results, inferring useful properties about the population. The compositional nature of differential privacy has motivated the design and implementation of several programming languages aimed at helping a data analyst in programming differentially private analyses. However, most of the programming languages for differential privacy proposed so far provide support for reasoning about privacy but not for reasoning about the accuracy of data analyses. To overcome this limitation, in this work we present DPella, a programming framework providing data analysts with support for reasoning about privacy, accuracy and their trade-offs. The distinguishing feature of DPella is a novel component which statically tracks the accuracy of different data analyses. In order to make tighter accuracy estimations, this component leverages taint analysis for automatically inferring statistical independence of the different noise quantities added for guaranteeing privacy. We evaluate our approach by implementing several classical queries from the literature and showing how data analysts can figure out the best manner to calibrate privacy to meet the accuracy requirements.