Heli: Heavy-Light Private Aggregation
Ryan Lehmkuhl, Henry Corrigan-Gibbs, Emma Dauterman, David J. Wu
摘要
This paper presents Heli, a system that lets a pair of servers collect aggregate statistics about private client-held data without learning anything more about any individual client's data. Like prior systems, Heli protects client privacy against a malicious server, protects correctness against misbehaving clients, and supports common statistical functions: average, variance, and more. Heli's innovation is that only one of the servers (the "heavy server") needs to do per-run work proportional to the number of clients; the other server (the "light server") does work sublinear in the number of clients, after a one-time setup phase. As a result, a computationally limited party, such as a low-budget non-profit, could potentially serve as the second server for a Heli deployment with millions of clients. Heli relies on a new cryptographic primitive, aggregationonly encryption, that allows computing certain restricted functions on many clients' encrypted data. In a deployment with ten million clients, in which the servers privately compute the sum of 32 client-held 1-bit integers, Heli's heavy server does 240,000 core-s of work and the light server does 7 core-ms of work. Compared with prior work, the heavy server does 38× more computation, but the light server does 120,000× less. Key generation. The decryptor samples random keys (ek 1 , . . . , ek 𝑛 ) for a key-homomorphic PRF with key-space Z 𝑝 . The de-
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它引用的顶会 Paper17
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- Lightweight Techniques for Private Heavy HittersDan Boneh, Elette Boyle, Henry Corrigan-Gibbs, Niv Gilboa 等S&P 2021 · 被引用 134 次
- EIFFeL: Ensuring Integrity for Federated LearningAmrita Roy Chowdhury, Chuan Guo, Somesh Jha, Laurens van der MaatenCCS 2022 · 被引用 70 次
- Private Summation in the Multi-Message Shuffle ModelBorja Balle, James Bell, Adrià Gascón, Kobbi NissimCCS 2020 · 被引用 52 次
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