集中式与本地差分隐私下数据流的持续发布
Continuous Release of Data Streams under both Centralized and Local Differential Privacy
AI总结：
针对实值数据流持续发布中的截断阈值与噪声问题，提出含阈值优化、扰动和平滑的隐私框架，并给出首个LDP流发布算法，效用较现有方法提升6至10个数量级。
摘要：
本文研究发布满足差分隐私（DP）的实值数据流这一问题。一个主要挑战在于数据的最大可能值可能非常大；因此有必要估计一个阈值，将超过该阈值的数值截断，以减少所有数据所需加入的噪声量。该估计必须以隐私方式基于数据完成。我们开发了这样一种方法，它使用指数机制（Exponential Mechanism），并采用一个质量函数；该函数能够很好地逼近效用目标，同时保持较低的敏感度。在给定阈值后，我们提出一种新颖的在线分层方法以及若干后处理技术。基于这些思想，我们将这些步骤形式化为一个用于流数据隐私发布的框架。该框架由三个组件组成：以隐私方式估计阈值的阈值优化器、向数据流中加入经过校准噪声的扰动器，以及利用后处理改进结果的平滑器。在该框架内，我们设计了一种满足被称为本地差分隐私（LDP）这一更严格DP设置的算法。据我们所知，这是首个用于发布流数据的LDP算法。通过使用四个真实世界数据集，我们证明，在效用方面（以回答随机范围查询的均方误差衡量），我们的机制比当前最先进方法优6至10个数量级。
英文摘要：
In this paper, we study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). One major challenge is that the maximal possible value can be quite large; thus it is necessary to estimate a threshold so that numbers above it are truncated to reduce the amount of noise that is required to all the data. The estimation must be done based on the data in a private fashion. We develop such a method that uses the Exponential Mechanism with a quality function that approximates well the utility goal while maintaining a low sensitivity. Given the threshold, we then propose a novel online hierarchical method and several post-processing techniques. Building on these ideas, we formalize the steps into a framework for private publishing of stream data. Our framework consists of three components: a threshold optimizer that privately estimates the threshold, a perturber that adds calibrated noises to the stream, and a smoother that improves the result using post-processing. Within our framework, we design an algorithm satisfying the more stringent setting of DP called local DP (LDP). To our knowledge, this is the first LDP algorithm for publishing streaming data. Using four real-world datasets, we demonstrate that our mechanism outperforms the state-of-the-art by a factor of 6-10 orders of magnitude in terms of utility (measured by the mean squared error of answering a random range query).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
