MPC-as-a-Service (MPCaaS) systems enable clients to outsource privacy-preserving computations to distributed servers, offering flexibility by adapting and configuring MPC protocols to meet diverse security requirements. However, traditional MPC protocols rely on a fixed set of servers for the entire computation process, limiting scalability. Dynamic MPC (DMPC) addresses this limitation by permitting participants to join or leave during the computation. Nevertheless, existing DMPC protocols assume synchronous networks, which can lead to failures under unbounded network delays. In this paper, we present AD-MPC, the first asynchronous dynamic MPC protocol. Our protocol ensures guaranteed output delivery under optimal resilience ((n = 3t + 1)). To achieve this, we introduce two critical components: an asynchronous dynamic preprocessing protocol that facilitates the on-demand generation of Beaver triples for secure multiplication, and an asynchronous transfer protocol that maintains consistency during party hand-offs. These components collectively ensure computation correctness and transfer consistency across participants. We implement AD-MPC and evaluate its performance across up to 20 geographically distributed nodes. Experimental results demonstrate that the protocol not only offers strong security guarantees in dynamic and asynchronous network environments but also achieves performance comparable to state-of-the-art DMPC protocols.

Recently, multi-behavior recommendation has become a hot topic in the field of recommendation systems. Yet, existing methods still face challenges in effectively representing multi-behavior semantic information from the following perspectives: (i) Previous works’ heavy reliance on a unified embedding for modeling all behavior interaction graphs hindered accuratemining of fine-grained user preference semantics across multiple behaviors. (ii) Existing multi-behavior contrastive learning (CL) tasks fail to capture the dependency of user preferring to items under different behaviors, thereby constrains the model’s ability in characterizing the personalized features of users/items. (iii) The rich semantic information in the knowledge graph is not fully leveraged. To address the above challenges, we design a Contrastive Learning-based Multi-behavior Recommendation with Semantic Knowledge Enhancement (CLMRS) framework, which consists of two encoding modules with CL tasks and a joint learning module. Specifically, in the multi-behavior meta-network encoding module, we propose a novel behavior-supervised graph convolutional encoder to fully mine the user preference semantics in each behavior. Meanwhile, in the semantic knowledge enhanced encoding module, we use a knowledge graph to provide more robust embeddings for items. Finally, we integrate the user/item embeddings learned by the two encoding modules into a comprehensive semantic vector through the joint learning module, which is used for the final prediction of potential users. Extensive experiments on four real-world datasets indicate that CLMRS consistently outperforms various state-of-the-art recommendation methods. Our model code is available at https://github.com/yuwenxuan3197/CLMRS.

Payment channel network (PCN) is a layer-two scaling solution that enables fast off-chain transactions but does not involve on-chain transaction settlement. PCNs raise new privacy issues including balance secrecy, relationship anonymity and payment privacy. Moreover, protecting privacy causes low transaction success rates. To address this dilemma, we propose zk-PCN, a privacy-preserving payment channel network using zk-SNARKs. We prevent from exposing true balances by setting up public balances instead. Using public balances, zk-PCN can guarantee high transaction success rates and protect PCN privacy with zero-knowledge proofs. Additionally, zk-PCN is compatible with the existing routing algorithms of PCNs. To support such compatibility, we propose zk-IPCN to improve zk-PCN with a novel proof generation (RPG) algorithm. zk-IPCN reduces the overheads of storing channel information and lowers the frequency of generating zero-knowledge proofs. Finally, extensive simulations demonstrate the effectiveness and efficiency of zk-PCN in various settings.