  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%.
