论文标题
联合子模型学习中的信息理论隐私
Information-Theoretic Privacy in Federated Submodel learning
论文作者
论文摘要
我们考虑在联合子模型学习中的信息理论隐私，其中全局服务器具有多个子模型。与传统的联合子模型学习中考虑的隐私相比，采用安全汇总以确保隐私，信息理论隐私为当地机器对子模型选择提供了更强的保护。我们提出了一个可实现的计划，该计划部分采用了传统的私人信息检索（PIR）方案，该方案可实现最低下载量。关于计算和通信开销，我们将可实现的方案与信息理论隐私进行了幼稚的方法进行比较。
We consider information-theoretic privacy in federated submodel learning, where a global server has multiple submodels. Compared to the privacy considered in the conventional federated submodel learning where secure aggregation is adopted for ensuring privacy, information-theoretic privacy provides the stronger protection on submodel selection by the local machine. We propose an achievable scheme that partially adopts the conventional private information retrieval (PIR) scheme that achieves the minimum amount of download. With respect to computation and communication overhead, we compare the achievable scheme with a naive approach for federated submodel learning with information-theoretic privacy.
