English(EN) Pushing Forward Multi-Secret-Key Homomorphic Encryption for Private Average Aggregation

新加密协议增强联邦学习中的隐私保护

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员开发了一种新的多密钥同态加密协议，旨在增强联邦学习中的隐私保护。该协议通过避免生成集体公钥，而是使用单独的密钥加密客户端更新，从而解决了现有方法的局限性。所提出的方法显著减少了私有平均聚合相关的密文膨胀和计算成本，与当前最先进的技术相比，性能有所提高。 AI

影响通过减少模型聚合过程中的数据泄露来增强联邦学习中的隐私保护。

排序理由该集群包含一篇详细介绍联邦学习新密码协议的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 23 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍联邦学习新密码协议的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, safety; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

arXiv cs.LG TIER_1 English(EN) · Miguel Morona-M\'inguez, Fernando P\'erez-Gonz\'alez, Alberto Pedrouzo-Ulloa · 2026-09-03 04:00

推动多密钥同态加密实现私有平均聚合

arXiv:2609.01945v1 Announce Type: cross Abstract: 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 build…