Towards Privacy-Preserving Federated Learning using Hybrid Homomorphic Encryption

Computer Science > Cryptography and Security [Submitted on 27 Mar 2026] Towards Privacy-Preserving Federated Learning using Hybrid Homomorphic Encryption Ivan Costa, Pedro Correia, Ivone Amorim, Eva Maia, Isabel Praça Federated Learning (FL) enables collaborative training while keeping sensitive data on clients' devices, but local model updates can still leak private information. Hybrid Homomorphic Encryption (HHE) has recently been applied to FL to mitigate client overhead while preserving privacy. However, existing HHE-FL systems rely on a single homomorphic key pair shared across all clients, which forces them to assume an unrealistically weak threat model: if a client misbehaves or intercepts another's traffic, private updates can be exposed. We eliminate this weakness by integrating two alternative key protection mechanisms into the HHE-FL workflow. The first is masking, where client keys are blinded before homomorphic encryption and later unblinded homomorphically by the server. The second is RSA encapsulation, where homomorphically encrypted keys are additionally wrapped under the server's RSA public key. These countermeasures prevent key misuse by other clients and extend HHE-FL security to adversarial settings with malicious participants. We implement both approaches on top of the Flower framework using the PASTA/BFV HHE scheme and evaluate them on the MNIST dataset with 12 clients. Results show that both mechanisms preserve model accuracy while adding minimal overhead: masking incurs negligible cost, and RSA encapsulation introduces only modest runtime and communication overhead. Comments: 25 pages, 4 figures, 24th International Conference on Applied Cryptography and Network Security (ACNS 2026) Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.26417 [cs.CR]   (or arXiv:2603.26417v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.26417 Focus to learn more Submission history From: Ivan Costa Silva [view email] [v1] Fri, 27 Mar 2026 13:46:29 UTC (1,381 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)