DEVA: Decentralized, Verifiable Secure Aggregation for Privacy-Preserving Learning
Published in In *24th Information Security Conference* (ISC), 2021
DEVA proposes a decentralized secure aggregation protocol that is both privacy-preserving and verifiable.
Key features:
- Ensures correctness of the aggregation result through non-interactive verification
- Protects individual inputs in federated learning settings
- Tolerates malicious users and decentralized trust assumptions
The paper demonstrates that DEVA achieves strong security guarantees without central authorities.
Recommended citation: Georgia Tsaloli, Bei Liang, Carlo Brunetta, Gustavo Banegas, Aikaterini Mitrokotsa. (2021). "DEVA: Decentralized, Verifiable Secure Aggregation for Privacy-Preserving Learning." In 24th Information Security Conference (ISC).
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