統合学習とホモモルフな暗号化に基づくクラウドプラットフォームのプライバシー保護最適化方法
1School of Computer Science and Engineering, Southeast University, Nanjing 211189, China.
Sensors (Basel, Switzerland)
|February 13, 2026
まとめ
この研究では,分散型トレーニングのセキュリティと効率性を向上させるヘテロゲネスな連邦化されたホモモルフな暗号化クラウド (HFHE-Cloud) モデルを紹介しています. HFHE-Cloudは,マルチテナントのクラウド環境で高いプライバシー保護とコンピューティングパフォーマンスを実現します.
科学分野:
- クラウドコンピューティング クラウドコンピューティング
- 分散型システム (Distributed Systems) とは,分散型システム (distributed systems) とは,分散型システム (distributed systems) とは,分散型システム (distributed system) とは,分散型システム (distributed system) とは,分散型システム (distributed system) とは,分散型システム (distributed system) とは,分散型システム (distributed system) とは,分散型システム (distributed system) とは,分散型システム (distributed system) とは,分散型システム (distributed system) とは,分散型システム (distributed system) とは,分散型システム (distributed system) とは,
- クリプトグラフィー クリプトグラフィー 暗号学
- 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.
背景:
- クラウド環境での分散型トレーニングは,プライバシーリーク,通信冗長性,マルチテナンスと高同時性による集積効率の低下などの課題に直面しています.
- 既存の統合学習モデルは,複雑なクラウドインフラストラクチャにおけるプライバシー保護とコンピューティングパフォーマンスのバランスをとるのに苦労しています.
研究 の 目的:
- プライバシー保護とコンピューティングパフォーマンスを最適化するクラウドプラットフォームのための安全で効率的な共同学習フレームワークを開発します.
- 暗号化計算と通信遅延に関連するパフォーマンスボトルネックに対処するために,分散型トレーニング.
主な方法:
- ホモモルフな暗号化と統合された学習 (FL) を統合したヘテロジェネスな連邦化されたホモモルフな暗号化クラウド (HFHE-Cloud) モデルを提案した.
- 性能のボトルネックを減らすために,異質なノードのための階層的なキーマッピングとダイナミックスケジューリングメカニズムを実装しました.
- 共同トレーニングプロセス中にオリジナルのデータを暴露しないことにより,データのプライバシーを保証します.
主要な成果:
- HFHE-Cloudは,総合的なパフォーマンスにおいてベースラインモデル (FedAvg,FedProx,FedPer,FedNova,HE-FedAvg) を大幅に上回りました.
- 94.25%までのグローバル精度と0.09以内の安定した損失で高いプライバシー保護を達成しました.
- 暗号化/復号の時間を約3分の1に短縮し,暗号化オーバーヘッドを13%削減することで,コンピューティングパフォーマンスを改善しました.
- 通信ラウンドを約5分の1削減し,ノード参加率の90%以上を維持することにより,分散型トレーニングの効率を高めました.
結論:
- HFHE-Cloudモデルは,高いセキュリティとスケーラビリティを証明し,複数テナントのクラウド環境に適しています.
- クラウドサービスプロバイダーと,強力なプライバシー保護と効率的なコラボレーティブトレーニングを求める企業にとって,実行可能な技術的ソリューションを提供します.
- クラウドプラットフォーム上の効率的な分散学習性能と,高度なプライバシー要求のバランスをうまく取っています.
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