隐私再访：形式化与实证评估
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation
摘要：
最近，一些方法和技术已被引入报告软件统计数据与强大的隐私保障。这些范围从抽象算法到具有不同假设的综合系统，并建立在本地差分隐私机制和匿名性之上。基于Encode-Shuffle-Analyze（ESA）框架，显着的结果正式澄清了隐私保障的重大改进，而不会通过匿名报告而损失效用。然而，这些结果要么是由具有看似不同的机制和攻击模型的系统组成，要么是对从业者几乎没有指导的正式声明。为了解决这个问题，我们提供了一个正式的治疗方法，并为匿名隐私保护报告提供了规范性的指导方针。我们重新审视了ESA框架与一个简单的，抽象的攻击者模型，以及假设涵盖它和其他拟议的匿名系统。根据新的正式隐私界限，我们研究了基于草图的编码和ESA机制的局限性，如数据依赖人群。我们还演示了如何欧空局的碎片化概念（报告数据方面的单独的，unconscious消息）提高隐私/效用权衡在本地和中央差分隐私保证。最后，为了帮助从业人员了解隐私保护报告的适用性和局限性，我们报告了大量的实证实验。我们使用具有重尾或近平坦分布的真实数据集，这对我们的技术构成了最大的困难;特别是，我们专注于从图像中提取的数据，这些数据可以很容易地以突出重建错误的方式可视化。为了展示这种方法的前景和独立的兴趣，我们还报告了使用匿名，隐私保护报告的实验，以训练高精度深度神经网络标准任务-MNIST和CIFAR-10。
英文摘要：
Recently, a number of approaches and techniques have been introduced for reporting software statistics with strong privacy guarantees. These range from abstract algorithms to comprehensive systems with varying assumptions and built upon local differential privacy mechanisms and anonymity. Based on the Encode-Shuffle-Analyze (ESA) framework, notable results formally clarified large improvements in privacy guarantees without loss of utility by making reports anonymous. However, these results either comprise of systems with seemingly disparate mechanisms and attack models, or formal statements with little guidance to practitioners. Addressing this, we provide a formal treatment and offer prescriptive guidelines for privacy-preserving reporting with anonymity. We revisit the ESA framework with a simple, abstract model of attackers as well as assumptions covering it and other proposed systems of anonymity. In light of new formal privacy bounds, we examine the limitations of sketch-based encodings and ESA mechanisms such as data-dependent crowds. We also demonstrate how the ESA notion of fragmentation (reporting data aspects in separate, unlinkable messages) improves privacy/utility tradeoffs both in terms of local and central differential-privacy guarantees. Finally, to help practitioners understand the applicability and limitations of privacy-preserving reporting, we report on a large number of empirical experiments. We use real-world datasets with heavy-tailed or near-flat distributions, which pose the greatest difficulty for our techniques; in particular, we focus on data drawn from images that can be easily visualized in a way that highlights reconstruction errors. Showing the promise of the approach, and of independent interest, we also report on experiments using anonymous, privacy-preserving reporting to train high-accuracy deep neural networks on standard tasks---MNIST and CIFAR-10.
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