With the growing privacy and data contamination concerns in recommendation systems, recommendation unlearning, i.e., unlearning the impact of specific learned data, has garnered more attention. Unfortunately, existing research primarily focuses on the complete unlearning of target data, neglecting the balance between unlearning integrity, practicality, and efficiency. Two major restrictions hinder the widespread application of this unlearning paradigm in practice. First, while prior studies often assume consistent similarity among samples, they overly emphasize the local collaborative relationships between samples and central nodes, leading to an imbalance between local and global collaborative information. Second, while data partition appears to be a default setup, this evidently exacerbates the sparsity of recommendation data, which can have a potentially negative impact on recommendation quality. To fill these gaps, this paper proposes a data partitioning and submodel training strategy, named Partition Distinction with Contrastive Recommendation Unlearning (PDCRU), which aims to balance data partitioning and feature sparsity. The key idea is to extract structural features as global collaborative information for samples and introduce structural feature constraints based on sample similarity during the partitioning process. For submodel training, we leverage contrastive learning to introduce additional high-quality training signals to enhance model embeddings. Extensive experiments validate the feasibility and consistent superiority of our method over existing recommendation unlearning models in learning and unlearning. Specifically, our model achieves a 4.83% improvement in performance and a 4.64x enhancement in unlearning efficiency compared to baseline methods. The code is released at https://github.com/linli0818/PDCRU.

The Uniform Resource Locator (URL) is a primary vector for numerous security threats, including phishing, malware propagation, and spam attacks, making URL-based analysis a critical task in security systems. However, existing research often focuses on static lexical features of individual URLs, overlooking deeper semantic, structural, and behavioral signals that can indicate malicious intent or evasive patterns. In this paper, we propose Behavior-Enhanced Semantic URL Embedding, a novel framework that integrates semantic, structural, and contextual information to improve the detection of security threats embedded in URLs. Our model is composed of three core modules: a semantic understanding module to extract token-level and contextual semantics, a topology structure learning module to capture hierarchical and sequential patterns of URL components, and a downstream multi-task adaptation module that fine-tunes embeddings with supervised contrastive learning for various security detection tasks. We evaluate our method across five public datasets covering key security applications such as malicious URL detection, phishing website identification, and spam filtering, consistently achieving superior performance over existing baselines. Additionally, we demonstrate the extensibility of our approach to related security tasks, showcasing its potential integration into real-world threat detection and security monitoring systems.

As the coverage of permissioned blockchains expands and the number of participating replicas increases, a scalable and efficient Byzantine Fault Tolerant (BFT) protocol is essential for large-scale blockchain. Unfortunately, previous BFT consensus protocols rely on a single leader to drive the protocol, which becomes a bottleneck for system scalability when the number of replicas exceeds a certain threshold. Although some proposals suggest hierarchically grouping nodes into different layers to alleviate verification pressure on the single leader. However, existing solutions only support serial execution between layers, causing performance and latency bottlenecks. To address these issues, we propose TierFlow, the first layered consensus protocol that supports pipelined execution, maintaining high throughput in scenarios with a large-scale deployment of replicas. TierFlow innovatively addresses the serial execution bottleneck in layered consensus by decoupling inter-layer consensus. To eliminate redundant phases, we use a pre-proof method to advance the next round of verification, and utilize delayed verification to merge similar verification workflows. We implement TierFlow and compare it with advanced BFT protocols such as HotStuff and Fast-HotStuff. We conduct extensive experiments with over 100 replicas, demonstrating that TierFlow achieves throughput 14x higher than Fast-HotStuff in large-scale application scenarios, with the performance disparity widening as scale increases.

The blockchain can offer a dependable and secure platform for Internet of Things (IoT) transactions with its distributed and secure network architecture. Unfortunately, it faces challenges, such as limited throughput, excessive computational costs, and high-transaction fees. Off-chain scaling protocols are used to address the scalability of blockchain for their outstanding performance and efficiency. To mitigate the high-cost interactions with blockchain, previous studies only considered moving transactions of payment hubs (PHs) off-chain, utilizing off-chain operators to aggregate multiple transactions. However, existing PHs overly rely on central operators for system maintenance, greatly increasing the risk of central operator failure (COF). Previous solutions allowed operators to submit unsettled state commitments (USCs) to the blockchain and overlooked the pessimistic scenario that could lead to state rollbacks. To address these issues, this article proposes an efficient multiparty payment protocol (HyperPay), aimed at utilizing the off-chain scaling technique to enhance transaction throughput and reduce on-chain cost. Specifically, we first propose a novel off-chain committee and collateral-based verifiable random leader election (C-VRE) to elect leaders fairly, thus mitigating the COF problem. Additionally, we design a new state validation mechanism and one-step fraud challenge (OSFC), enabling verifiers to directly construct fraud proofs and challenges on-chain, thereby preventing leaders from submitting USC. Our evaluation indicates that HyperPay reduces on-chain costs of challenge by 80% and boosts peak throughput by a factor of 10X-283X. A comprehensive theoretical analysis and experimental results substantiate the security and effectiveness of our proposed approach.