Metraipolis › archive › dc_35e643ea04e5

Pushing Forward Multi-Secret-Key Homomorphic Encryption for Private Average Aggregation — arXiv:2609.01945

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.01945
License (as archived): CC0-1.0
Archived: 2026-10-17T04:40:12.579Z
SHA-256 of the archived text: c8492f011c574c1686c94af83d8fcd704c512a63a73a39cb87c8cd47de6602de
Stable URL of this page: https://metraipolis.com/documento/dc_35e643ea04e5

How to cite

Pushing Forward Multi-Secret-Key Homomorphic Encryption for Private Average Aggregation — arXiv:2609.01945 — arXiv (en), https://arxiv.org/abs/2609.01945 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_35e643ea04e5 (Metraipolis archive dc_35e643ea04e5, sha256 c8492f011c574c1686c94af83d8fcd704c512a63a73a39cb87c8cd47de6602de)

BibTeX

@misc{dc35e643ea04e5,
  title = {Pushing Forward Multi-Secret-Key Homomorphic Encryption for Private Average Aggregation — arXiv:2609.01945},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.01945},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_35e643ea04e5; sha256 c8492f011c574c1686c94af83d8fcd704c512a63a73a39cb87c8cd47de6602de},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.01945. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Federated Learning enables multiple clients to train a shared model while keeping their local datasets isolated. However, the exchanged model updates may still leak sensitive information, making private aggregation a central building block in practical deployments, especially in the cross-silo setting. Homomorphic Encryption naturally fits the client--aggregator communication pattern of Federated Learning, but conventional single-key deployments rely on strong non-collusion assumptions. Multiparty Homomorphic Encryption removes this limitation, although recent attacks under restricted decryption access require large-variance smudging noise during collaborative decryption, which significantly increases ciphertext size and implementation complexity. In this work, we propose lightweight multi-secret-key protocols for private average aggregation based on RLWE-based Homomorphic Encryption. Our construction departs from the usual multiparty blueprint by avoiding the generation of a collective public key. Instead, each client encrypts its update under its own secret key, while the resulting ciphertexts remain compatible with homomorphic aggregation and collaborative decryption. By explicitly tracking and cancelling the ciphertext noise during decryption, the protocol removes the need for large $\lambda$-dependent smudging noise. We instantiate the construction with both exact BFV-based and approximate CKKS-based variants, prove its security in the semi-honest model against an adversary corrupting the aggregator and up to $L-1$ clients, and compare its communication and runtime performance with state-of-the-art MHE-based aggregation. Our results show that the proposed approach substantially reduces ciphertext expansion and online cost, while preserving practical homomorphic aggregation performance.

Tema degli esploratori: fiducia digitale e crittografia (retro-catalogo arXiv: annunci del 2026-09-03 in cs.CR).
Categorie arXiv: Cryptography and Security (cs.CR) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.01945 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T04:09:09.342Z dalla pagina https://arxiv.org/catchup/cs.CR/2026-09-03?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).