Federated analytics has many applications in edge computing, its use can lead to better decision making for service provision, product development, and user experience. We propose a Bayesian approach to trend detection in which the probability of a keyword being trendy, given a dataset, is computed via Bayes' Theorem; the probability of a dataset, given that a keyword is trendy, is computed through secure aggregation of such conditional probabilities over local datasets of users. We propose a protocol, named SAFE, for Bayesian federated analytics that offers sufficient privacy for production grade use cases and reduces the computational burden of users and an aggregator. We illustrate this approach with a trend detection experiment and discuss how this approach could be extended further to make it production-ready.

中文翻译：

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用于隐私保护趋势检测的安全贝叶斯联合分析

联合分析在边缘计算中有许多应用，它的使用可以为服务提供、产品开发和用户体验做出更好的决策。我们提出了一种用于趋势检测的贝叶斯方法，其中在给定数据集的情况下，通过贝叶斯定理计算关键字流行的概率；假设关键字是流行的，数据集的概率是通过对用户本地数据集的这种条件概率的安全聚合来计算的。我们为贝叶斯联合分析提出了一个名为 SAFE 的协议，该协议为生产级用例提供了足够的隐私，并减少了用户和聚合器的计算负担。我们通过趋势检测实验来说明这种方法，并讨论如何进一步扩展这种方法以使其可用于生产。