Recommender systems (RSs) have become crucial in helping users navigate the vast amount of online content available today. Graph neural networks (GNNs) have been applied to RSs to capture complex user–item relationships, but existing methods compromise privacy or require centralized data storage. Current works attempt to perform GNN-based RSs under federated learning settings to prevent privacy leakage. However, these works need to perform explicit graph propagation during training, which still introduces potential privacy leakage and data collusion. To address these challenges, we propose a Laplacian-based model called Laplacian graded link prediction (LapGLP) that leverages infinite graph propagation with a constant weight matrix. Instead of actually performing infinite graph propagation, the model abstracts the underlying relations between embeddings after propagation with a weighted minimum squared error problem. Furthermore, we propose a federated framework named FedLapGLP to improve privacy in federated GNN-based RSs, which splits the objective loss function into independent parts that are calculated by each user. Experimental comparisons with state-of-the-art federated RS methods demonstrate the advantages of our proposed approach in terms of high-order connectivity, comprehensive graph information, social relations, full-interaction protection, collusion resistance, and user-embedding protection. The implementation of the proposed framework is available at https://github.com/Limhady/LapGLP.

Blockchain rewriting has become widely explored for addressing data deletion requirements, such as error data deletion, space-saving, and compliance with the “right-to-be-forgotten” rule. However, existing approaches are inadequate for handling cross-chain redaction issues, issues, in facing with the increasing need for inter-chain communication. In particular, transaction rewriting on a blockchain might have relevant effects on the states of other blockchains. The cross-chain interoperability results in inter-chain transactions with more complex dependency relations. The issues pose new challenges to achieve rewriting consistency, for example, ensuring the rewriting of related transactions when a transaction is being modified, and achieve atomic rewriting, whereby two cross-chain transactions must either all, or neither, be processed. This paper introduces a cross-chain solution IvyRedaction, with an emphasis on customizing a decentralized intermediary for generating and maintaining global cross-chain redaction states and transaction dependencies. The paper proposes a novel cross-chain state mapping method with rollback rules, as well as customized block structures and verification algorithms, to address the aforementioned issues. Proof-of-concept experiments are conducted to demonstrate the feasibility of the proposed framework.