USENIX Security2026Top-tier venue
Heli: Heavy-Light Private Aggregation
Ryan Lehmkuhl, Henry Corrigan-Gibbs, Emma Dauterman, David J. Wu
Abstract
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-
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 10dda11f-6924-42e7-9833-bcd151ecb42eBuilds on17
- Practical Secure Aggregation for Privacy-Preserving Machine LearningKallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone et al.CCS 2017 · 3,936 citations
- Bulletproofs: Short Proofs for Confidential Transactions and MoreBenedikt Bünz, Jonathan Bootle, Dan Boneh, Andrew Poelstra et al.S&P 2018 · 1,285 citations
- Lightweight Techniques for Private Heavy HittersDan Boneh, Elette Boyle, Henry Corrigan-Gibbs, Niv Gilboa et al.S&P 2021 · 134 citations
- EIFFeL: Ensuring Integrity for Federated LearningAmrita Roy Chowdhury, Chuan Guo, Somesh Jha, Laurens van der MaatenCCS 2022 · 70 citations
- Private Summation in the Multi-Message Shuffle ModelBorja Balle, James Bell, Adrià Gascón, Kobbi NissimCCS 2020 · 52 citations
Related papers
- Private Analytics via Streaming, Sketching, and Silently Verifiable ProofsMayank Rathee, Yuwen Zhang, Henry Corrigan-Gibbs, Raluca Ada PopaS&P 2024 · 8 citations
- POPSTAR: Lightweight Threshold Reporting with Reduced LeakageHanjun Li, Sela Navot, Stefano TessaroUSENIX Security 2024 · 5 citations
- Communication-Efficient Private Join and Compute over Distributed Input SetsYunqing Sun, Xinran Cai, Hanlin Liu, Xiao Wang et al.CCS 2026
- Secure Single-Server Aggregation with (Poly)Logarithmic OverheadJames Henry Bell, Kallista A. Bonawitz, Adrià Gascón, Tancrède Lepoint et al.CCS 2020 · 13 citations
- Samplable Anonymous Aggregation for Private Federated Data AnalysisKunal Talwar, Shan Wang, Audra McMillan, Vitaly Feldman et al.CCS 2024 · 6 citations
