面向去中心化Web 3.0应用的联邦分析赋能的频繁模式挖掘
Federated Analytics-Empowered Frequent Pattern Mining for Decentralized Web 3.0 Applications
AI总结：
本文提出FedWeb，一种针对Web 3.0频繁模式挖掘的联邦分析设计，基于分布式差分隐私和候选过滤方案，在保证结果正确性的同时显著减少参与数据所有者数量并提升数据效用。
摘要：
新兴的Web 3.0范式旨在去中心化现有的Web服务，实现透明性、激励和隐私保护等理想特性。然而，当前由区块链基础设施支持的Web 3.0应用仍无法以可扩展且保护隐私的方式支持复杂的数据分析任务。本文将新兴的联邦分析（FA）范式引入Web 3.0服务领域，使数据能够保留在本地，同时仍以保护隐私的方式为复杂的Web分析任务做出贡献。我们提出了FedWeb，一种针对Web 3.0中重要频繁模式挖掘任务量身定制的FA设计。FedWeb基于一种新颖的分布式差分隐私技术，显著减少了支持隐私保护的Web 3.0数据分析所需的参与数据所有者数量。挖掘结果的正确性由基于Hoeffding不等式和Chebychev不等式的理论严谨的候选过滤方案保证。提出了两种响应预算节省方案以进一步减少参与的数据所有者。在三个代表性Web 3.0场景上的实验表明，FedWeb能够将数据效用提高约25.3%，并将参与的数据所有者减少约98.4%。
英文摘要：
The emerging Web 3.0 paradigm aims to decentralize existing web services, enabling desirable properties such as transparency, incentives, and privacy preservation. However, current Web 3.0 applications supported by blockchain infrastructure still cannot support complex data analytics tasks in a scalable and privacy-preserving way. This paper introduces the emerging federated analytics (FA) paradigm into the realm of Web 3.0 services, enabling data to stay local while still contributing to complex web analytics tasks in a privacy-preserving way. We propose FedWeb, a tailored FA design for important frequent pattern mining tasks in Web 3.0. FedWeb remarkably reduces the number of required participating data owners to support privacy-preserving Web 3.0 data analytics based on a novel distributed differential privacy technique. The correctness of mining results is guaranteed by a theoretically rigid candidate filtering scheme based on Hoeffding's inequality and Chebychev's inequality. Two response budget saving solutions are proposed to further reduce participating data owners. Experiments on three representative Web 3.0 scenarios show that FedWeb can improve data utility by ~25.3% and reduce the participating data owners by ~98.4%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
