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SOSP2023顶会

Arboretum: A Planner for Large-Scale Federated Analytics with Differential Privacy

Elizabeth Margolin, Karan Newatia, Tao Luo, Edo Roth, Andreas Haeberlen

2023年份
3被引次数
4顶会引用

摘要

Federated analytics is a way to answer queries over sensitive data that is spread across multiple parties, without sharing the data or collecting it in a single place. Prior work has developed solutions that can scale to large deployments with millions of devices but, due to the distributed nature of federated analytics, these solutions can support only a limited class of queries - typically various forms of numerical queries, which can be answered with lightweight cryptographic primitives. Supporting richer queries, such as categorical queries, requires heavier cryptography, whose cost can quickly exceed even the resources of a powerful data center.

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