Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacy

Temel Kavramlar

Proposing AerisAI for secure and efficient decentralized collaborative AI with differential privacy.

Özet

最近のフェデレーテッドラーニング（FL）の概念に基づいて、信頼性を向上させるために新しい分散人工知能（AI）パラダイムが導入されました。しかし、多くの現行のFLシステムは、信頼できる第三者の必要性によりデータプライバシーの問題に直面しています。本研究では、ホモモーフィック暗号化と細かい粒度の差分プライバシーを使用してセキュリティを向上させるために、新しい分散協力型AIフレームワーク「AerisAI」を提案します。AerisAIは、ブロックチェーンベースのスマートコントラクトを使用して暗号化されたパラメーターを直接集約し、信頼できる第三者の必要性を排除します。また、異なるサービスレベル契約に基づいて細かい粒度のアクセス制御を実現するためにCP-ABEに基づくグループキー管理も提供します。

İstatistikler

提案手法は他の最先端手法よりも優れた結果を示す。
実験結果は、提案手法が他の最先端手法よりも優れていることを示している。
AerisAIは他の状況やデータセットでも堅牢であり適用可能であることが示唆されている。

Alıntılar

"Decentralization. To address the weakness of requiring a centralized server, we employ blockchain to build a decentralized platform."
"Privacy preservation. We perturb the gradients by adding noise to prevent the real values of the gradients from being exposed on blockchain."
"Auditability. All the transactions on the blockchain are recorded and can be audited by all the clients."

Önemli Bilgiler Şuradan Elde Edildi

Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacy

by Lo-Yao Yeh,S... : arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00023.pdf

Daha Derin Sorular

How does AerisAI ensure scalability in encrypting aggregated noise for multiple clients

AerisAI ensures scalability in encrypting aggregated noise for multiple clients by implementing group key management based on ciphertext policy attribute-based encryption (CP-ABE). This approach allows the oracle to efficiently distribute the encrypted aggregated noise to all clients. Instead of individually encrypting and distributing the noise to each client, CP-ABE enables the oracle to broadcast the encrypted noise using a specific policy. By utilizing this method, AerisAI can handle a large number of clients participating in model training without experiencing scalability issues.

What potential challenges might arise when implementing AerisAI in real-world applications beyond the experimental setting

When implementing AerisAI in real-world applications beyond the experimental setting, several challenges may arise. One challenge is ensuring compatibility with existing systems and infrastructure within organizations. Integration with different data sources, platforms, and technologies may require significant effort and customization. Additionally, maintaining data privacy compliance with regulations such as GDPR or HIPAA could pose challenges due to varying legal requirements across regions.
Another challenge is optimizing performance while preserving security features. As datasets grow larger or more complex models are used, ensuring efficient communication between clients and the blockchain network becomes crucial. Balancing computational resources for encryption/decryption processes with model training tasks will be essential for achieving both high performance and robust security.
Furthermore, addressing potential adversarial attacks or vulnerabilities in the system is critical for long-term success. Continuous monitoring of system behavior, threat detection mechanisms, and regular security audits will be necessary to mitigate risks associated with malicious actors attempting to compromise data privacy or disrupt operations.

How can AerisAI adapt to different types of datasets and models to maintain its performance and security features

AerisAI can adapt to different types of datasets and models while maintaining its performance and security features through several strategies:

Customized Encryption Techniques: Depending on the characteristics of the dataset (e.g., size, complexity) and model architecture (e.g., neural network structure), AerisAI can employ customized encryption techniques tailored to specific requirements. For example, adjusting parameters in homomorphic encryption schemes based on dataset properties can optimize performance without compromising security.

Dynamic Privacy Settings: Implementing dynamic privacy settings that adjust according to dataset sensitivity levels can help AerisAI maintain optimal privacy protection while accommodating diverse data types. By fine-tuning differential privacy parameters based on dataset attributes, AerisAI can ensure appropriate levels of confidentiality without sacrificing accuracy.

Model Optimization Strategies: Utilizing optimization techniques such as federated learning algorithms that prioritize certain aspects of model training over others based on dataset characteristics can enhance overall performance efficiency while upholding security standards.