Abstract
This paper introduces LERNA, a new framework for single-server secure aggregation. Our protocols are tailored to the setting where multiple consecutive aggregation phases are performed with the same set of clients, a fraction of which can drop out in some of the phases. We rely on an initial secret sharing setup among the clients which is generated once-and-for-all, and reused in all following aggregation phases. Compared to prior works [Bonawitz et al. CCS’17, Bell et al. CCS’20], the reusable setup eliminates one round of communication between the server and clients per aggregation—i.e., we need two rounds for semi-honest security (instead of three), and three rounds (instead of four) in the malicious model. Our approach also significantly reduces the server’s computational costs by only requiring the reconstruction of a single secret-shared value (per aggregation). Prior work required reconstructing a secret-shared value for each client involved in the computation.
We provide instantiations of LERNA based on both the Decisional Composite Residuosity (DCR) and (Ring) Learning with Rounding ((R)LWR) assumptions respectively and evaluate a version based on the latter assumption. In addition to savings in round-complexity (which result in reduced latency), our experiments show that the server computational costs are reduced by two orders of magnitude in comparison to the state-of-the-art. In settings with a large number of clients, we also reduce the computational costs up to twenty-fold for most clients, while a small set of “heavy clients” is subject to a workload that is still smaller than that of prior work.
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Notes
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More specifically, using the protocol of Bell et. al., if the server is malicious, it may recover the sums of inputs of multiple subsets of clients.
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Running code provided at https://lwe-estimator.readthedocs.io.
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Acknowledgement
This paper was prepared in part for information purposes by the Artificial Intelligence Research group and AlgoCRYPT CoE of JPMorgan Chase & Co and its affiliates (“JP Morgan”), and is not a product of the Research Department of JP Morgan. JP Morgan makes no representation and warranty whatsoever and disclaims all liability, for the completeness, accuracy or reliability of the information contained herein. This document is not intended as investment research or investment advice, or a recommendation, offer or solicitation for the purchase or sale of any security, financial instrument, financial product or service, or to be used in any way for evaluating the merits of participating in any transaction, and shall not constitute a solicitation under any jurisdiction or to any person, if such solicitation under such jurisdiction or to such person would be unlawful. 2023 JP Morgan Chase & Co. All rights reserved.
Hanjun Li was supported by a NSF grant CNS-2026774 and a Cisco Research Award.
Huijia Lin was supported by NSF grants CNS-1936825 (CAREER), CNS-2026774, a JP Morgan AI Research Award, a Cisco Research Award, and a Simons Collaboration on the Theory of Algorithmic Fairness.
Stefano Tessaro was supported in part by NSF grants CNS-2026774, CNS-2154174, a JP Morgan Faculty Award, a CISCO Faculty Award, and a gift from Microsoft.
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Li, H., Lin, H., Polychroniadou, A., Tessaro, S. (2023). LERNA: Secure Single-Server Aggregation via Key-Homomorphic Masking. In: Guo, J., Steinfeld, R. (eds) Advances in Cryptology – ASIACRYPT 2023. ASIACRYPT 2023. Lecture Notes in Computer Science, vol 14438. Springer, Singapore. https://doi.org/10.1007/978-981-99-8721-4_10
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