Abstract
Aggregating data from multiple sources is often required in multiple applications. In this paper, we introduce \(\textsf {DEVA}\), a protocol that allows a distributed set of servers to perform secure and verifiable aggregation of multiple users’ secret data, while no communication between the users occurs. \(\textsf {DEVA}\) computes the sum of the users’ input and provides public verifiability, i.e., anyone can be convinced about the correctness of the aggregated sum computed from a threshold amount of servers. A direct application of the \(\textsf {DEVA}\) protocol is its employment in the machine learning setting, where the aggregation of multiple users’ parameters (used in the learning model), can be orchestrated by multiple servers, contrary to centralized solutions that rely on a single server. We prove the security and verifiability of the proposed protocol and evaluate its performance for the execution time and bandwidth, the verification execution, the communication cost, and the total bandwidth usage of the protocol. We compare our findings to the prior work, concluding that \(\textsf {DEVA}\) requires less communication cost for a big amount of users.
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Notes
- 1.
If \(m\not \mid n\), then \(\mid \varGamma _j \mid =\lceil \frac{n}{m}\rceil \) for \(j\in [1,m-1]\) and \(\mid \varGamma _m \mid =n-(m-1)\lceil \frac{n}{m}\rceil \).
- 2.
\(\mathcal {A}\) must know the secret key by either breaking the key agreement security or by maliciously corrupting the user, e.g., by personally creating it.
- 3.
All code will be released publicly after publication, but is already available to reviewers upon request through the program committee.
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Acknowledgement
This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.
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Tsaloli, G., Liang, B., Brunetta, C., Banegas, G., Mitrokotsa, A. (2021). \(\textsf {DEVA}\): Decentralized, Verifiable Secure Aggregation for Privacy-Preserving Learning. In: Liu, J.K., Katsikas, S., Meng, W., Susilo, W., Intan, R. (eds) Information Security. ISC 2021. Lecture Notes in Computer Science(), vol 13118. Springer, Cham. https://doi.org/10.1007/978-3-030-91356-4_16
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