为Web3.0设计可信去中心化缓存框架，提升访问效率与安全性。
TDC-Cache: A Trustworthy Decentralized Cooperative Caching Framework for Web3.0
- 构建双层架构，用去中心化预言机网络作可信中介
- 动态优化缓存策略，降低20%延迟，缓存命中率最高提升18%
- 提出共识机制保障缓存一致性，适合研究去中心化系统者
Web3.0的快速发展正推动互联网从集中式向去中心化转型，赋予用户对其数据的完全主权。然而，在去中心化数据访问中，冗余数据复制导致效率下降，数据不一致引发安全漏洞。为此，我们提出可信去中心化协同缓存（TDC-Cache）框架，以实现高效缓存并增强系统抗攻击能力。该框架采用两层架构，其中去中心化预言机网络（DON）层作为可信中间平台，连接去中心化存储与用户请求。针对Web3.0复杂网络拓扑与数据流，提出基于深度强化学习的去中心化缓存（DRL-DC）方法，动态优化分布式预言机的缓存策略。同时，设计了合作学习证明（PoCL）共识机制，确保DON内缓存决策的一致性。实验表明，相比现有方法，该框架平均访问延迟降低20%，缓存命中率最高提升18%，平均成功共识率提高10%。本工作首次系统探索Web3.0去中心化缓存框架与策略。
原文摘要 · Abstract (English)
The rapid growth of Web3.0 is transforming the Internet from a centralized structure to decentralized, which empowers users with unprecedented self-sovereignty over their own data. However, in the context of decentralized data access within Web3.0, it is imperative to cope with efficiency concerns caused by the replication of redundant data, as well as security vulnerabilities caused by data inconsistency. To address these challenges, we develop a Trustworthy Decentralized Cooperative Caching (TDC-Cache) framework for Web3.0 to ensure efficient caching and enhance system resilience against adversarial threats. This framework features a two-layer architecture, wherein the Decentralized Oracle Network (DON) layer serves as a trusted intermediary platform for decentralized caching, bridging the contents from decentralized storage and the content requests from users. In light of the complexity of Web3.0 network topologies and data flows, we propose a Deep Reinforcement Learning-Based Decentralized Caching (DRL-DC) for TDC-Cache to dynamically optimize caching strategies of distributed oracles. Furthermore, we develop a Proof of Cooperative Learning (PoCL) consensus to maintain the consistency of decentralized caching decisions within DON. Experimental results show that, compared with existing approaches, the proposed framework reduces average access latency by 20%, increases the cache hit rate by at most 18%, and improves the average success consensus rate by 10%. Overall, this paper serves as a first foray into the investigation of decentralized caching framework and strategy for Web3.0.
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