Privacy-preserving data trading allows data owners to sell data to consumers through a data trading web platform, the data market, without disclosing sensitive information in raw data. It enables legitimate data transmission and aggregation, facilitating large-scale data-driven model training. However, existing differential privacy-based approaches struggle to inject precisely calibrated noise in a trustworthy manner without revealing raw data to a third party, thus making them fail in achieving strong fairness and controllable privacy simultaneously, especially when facing malicious external adversaries or a corrupted data market.

With the rapid advancement of Web 3.0 technologies, public blockchain platforms are witnessing the emergence of novel services designed to enhance user privacy and anonymity. However, the powerful untraceability features inherent in these services inadvertently make them attractive tools for criminals seeking to launder illicit funds. Notably, existing de-anonymization methods face three major challenges when dealing with such transactions: highly homogenized transactional semantics, limited ability to model temporal discontinuities, and insufficient consideration of structural sparsity in account association graphs. To address these, we propose GradWATCH, designed to track anonymous accounts in Ethereum privacy-preserving services. Specifically, we first design a learnable account feature mapping module to extract informative transactional semantics from raw on-chain data. We then incorporate transaction relations into the account association graph to alleviate the adverse effects of structural sparsity. To capture temporal evolution, we further propose an edge-aware sliding-window mechanism that propagates and updates gradients at three granularities. Finally, we identify accounts controlled by the same entity by measuring their embedding distances in the learned representation space. Experimental results show that even under the conditions of unbalanced labels and sparse transactions, GradWATCH still achieves significant performance gains, with relative improvements ranging from 1.62% to 15. 22% in the MRR and from 3. 85% to 7. 31% in the F_1.

Federated learning is susceptible to model poisoning attacks, especially those meticulously crafted for servers. Traditional defense methods mainly focus on updating assessments or robust aggregation against manually crafted myopic attacks. When facing advanced attacks, their defense stability is notably insufficient. Therefore, it is imperative to develop adaptive defenses against such advanced poisoning attacks. We find that benign clients exhibit significantly higher data distribution stability than malicious clients in federated learning in both CV and NLP tasks. Therefore, the malicious clients can be recognized by observing the stability of their data distribution. In this paper, we propose AdaAggRL, an RL-based Adaptive Aggregation method, to defend against sophisticated poisoning attacks. Specifically, we first utilize distribution learning to simulate the clients' data distributions. Then, we use maximum mean discrepancy (MMD) to calculate the pairwise similarity of the current local model data distribution, its historical data distribution, and global model data distribution. Finally, we use policy learning to adaptively determine the aggregation weights based on the above similarities. Experiments on four real-world datasets demonstrate that the proposed defense model significantly outperforms widely adopted defense models for sophisticated attacks.

The scaled Web 3.0 digital economy, represented by decentralized finance (DeFi), has sparked increasing interest in the past few years, which usually relies on blockchain for token transfer and diverse transaction logic. However, illegal behaviors, such as financial fraud, hacker attacks, and money laundering, are rampant in the blockchain ecosystem and seriously threaten its integrity and security. In this paper, we propose a novel double graph-based Ethereum account de-anonymization inference method, dubbed DBG4ETH, which aims to capture the behavioral patterns of accounts comprehensively and has more robust analytical and judgment capabilities for current complex and continuously generated transaction behaviors. Specifically, we first construct a global static graph to build complex interactions between the various account nodes for all transaction data. Then, we also construct a local dynamic graph to learn about the gradual evolution of transactions over different periods. Different graphs focus on information from different perspectives, and features of global and local, static and dynamic transaction graphs are available through DBG4ETH. In addition, we propose an adaptive confidence calibration method to predict the results by feeding the calibrated weighted prediction values into the classifier. Experimental results show that DBG4ETH achieves state-of-the-art results in the account identification task, improving the F1-score by at least 3.75% and up to 40.52% compared to processing each graph type individually and outperforming similar account identity inference methods by 5.23 % to 12.91 %.

In the Web 3.0 ecosystem, blockchain and Artificial Intelligence of Things (AIoT) construct the infrastructure, where blockchain transaction semantic detection (BTSD) aims to enhance blockchain security by identifying illegal transactions through distributed miners executing AI algorithms. However, the computational cost of performing semantic detection discourages miners from participating without adequate incentives. Existing studies focus on algorithmic aspects of BTSD, which generally ignore the critical issue of incentive mechanism. To fill this gap, we propose the first incentive-based BTSD framework in the transaction pool phase, emphasizing how incentives affect the behavior of miners and users. We use evolutionary game theory to model miner-user interactions and define three key scenarios to simulate the impact of reward decay and penalty factors on system dynamics. Our results demonstrate that adjusting these parameters significantly influences the number of miners engaging in semantic detection and users initiating legitimate transactions. Under certain conditions, a well-designed incentive mechanism can lead to an Evolutionary Stable Strategy (ESS), thereby achieving systemic stability. This study introduces a novel incentive mechanism for BTSD during the transaction pool phase and validates its effectiveness through both theoretical insights and numerical solutions to enhance blockchain security.

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.

Private data, being larger and quality-higher than public data, can greatly improve large language models (LLM).However, due to privacy concerns, this data is often dispersed in multiple silos, making its secure utilization for LLM training a challenge.Federated learning (FL) is an ideal solution for training models with distributed private data, but traditional frameworks like FedAvg are unsuitable for LLM due to their high computational demands on clients.An alternative, split learning, offloads most training parameters to the server while training embedding and output layers locally, making it more suitable for LLM.Nonetheless, it faces significant challenges in security and efficiency.Firstly, the gradients of embeddings are prone to attacks, leading to potential reverse engineering of private data.Furthermore, the server's limitation of handle only one client's training request at a time hinders parallel training, severely impacting training efficiency.In this paper, we propose a Federated Learning framework for LLM, named FL-GLM, which prevents data leakage caused by both serverside and peer-client attacks while improving training efficiency.Specifically, we first place the input block and output block on local client to prevent embedding gradient attacks from server.Secondly, we employ key-encryption during client-server communication to prevent reverse engineering attacks from peer-clients.Lastly, we employ optimization methods like client-batching or server-hierarchical, adopting different acceleration methods based on the actual computational capabilities of the server.Experimental results on NLU and generation tasks demonstrate that FL-GLM achieves comparable metrics to centralized chatGLM model, validating the effectiveness of our federated learning framework.

In cross-silo federated learning (FL), due to the heterogeneous participants, free-riders can utilize information asymmetry to make profits without performing any local model training. Free-riding attack poses possibilities and opportunities for unfairness and can seriously impair the operation of the FL ecosystem. It motivates our work to explore and characterize the unique features of free-riding attack, which differ from other attacks such as poisoning attacks. In this paper, we propose an evolutionary public goods game-based incentive model (Fed-EPG), which makes the first attempt to construct the interaction model among the participants through the evolutionary public goods game. Specifically, we consider both the public good characteristics of cross-silo FL models as well as the bounded rationality and incomplete information of competitors. We first introduce asymmetric environmental feedback to represent reward and punishment strategies in evolutionary game, and then adopt a multi-segment nonlinear control method to dynamically adjust the rewards and punishments among the participants, which achieves the incentive for the participants to cooperate stably during the training process. Experimental results validate that our incentive model is effective in the mitigation of free-riding. attacks.

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.

Off-chain transactions seek to address the low on-chain scalability and enable blockchain-based payments over unreliable on-chain networks. The key problem with existing works is that they fail to balance security and flexibility in their designs. These studies would have been more useful if they could provide a sense of security without compromising their flexibility. We hypothesize that two offline parties having loosely synchronized clocks and channels with known bounded latency can conduct off-chain transactions while maintaining a high level of security and flexibility: we introduce a novel blockchain-based offline payment protocol that supports our hypothesis. Our work leverages on-chain smart contracts and offline wallet interactions to build resilience against intermittent on-chain connectivity. Our protocol achieves flexible and trusted computations with the use of platform-agnostic Trusted Execution Environments (TEEs) and open transactions. We empirically evaluate our design over the mainstream Intel Software Guard Extensions (SGX) and compare our protocol with state-of-the-art solutions. We found that our protocol attains high efficiency and exhibits an advanced level of security and flexibility in functionality. We evaluate our construction against several real-world attacks. We prove the security and robustness of our scheme based on a practical universally composable framework with synchronous settings. This work contributes to the existing knowledge of safe and user-friendly offline payment solutions for the blockchain technology.

The Practical Byzantine Fault Tolerance (PBFT) protocol-supported blockchain can provide decentralized security and trust mechanisms for the Internet of Things (IoT). However, the PBFT protocol is not specifically designed for IoT applications. Consequently, adapting PBFT to the dynamic changes of an IoT environment with incomplete information represents a challenge that urgently needs to be addressed. To this end, we introduce DA-PBFT, a PBFT dynamic adaptive framework based on a multi-agent architecture. DAPBFT divides the dynamic adaptive process into two sub-processes: optimality-seeking and optimization decision-making. During the optimality-seeking process, a PBFT optimization model is constructed based on deep reinforcement learning. This model is designed to generate PBFT optimization strategies for consensus nodes. In the optimization decision-making process, a PBFT optimization decision consensus mechanism is constructed based on the Borda count method. This mechanism ensures consistency in PBFT optimization decisions within an environment characterized by incomplete information. Furthermore, we designed a dynamic adaptive incentive mechanism to explore the Nash equilibrium conditions and security aspects of DA-PBFT. The experimental results demonstrate that DA-PBFT is capable of achieving consistency in PBFT optimization decisions within an environment of incomplete information, thereby offering robust and efficient transaction throughput for IoT applications.

Blockchain store states in Log-Structured Merge (LSM) tree-based database. Due to blockchain traceability, the growing ancient states are inevitably stored in the databases. Unfortunately, by default, this process mixescurrentandancientstates in the data layout, increasing unnecessary disk I/O access and slowing transaction execution. This paper proposes MoltDB, a scalable LSM-based database for efficient transaction execution through a novel idea ofancient state segregation, i.e., to segregate current and ancient states in the data layout. However, the frequently generated and uncertainly accessed characteristics of ancient states make the segregation challenging. Thus, we develop an “extract-compact” mechanism to batch extraction process for frequently generated ancient states and the LSM compaction process to relieve additional disk I/O overhead. Moreover, we design an adaptive LSM-based storage for the uncertainly accessed ancient states extracted for on-demand access. We implement MoltDB as a database engine compatible with many mainstream blockchains and integrate it into Ethereum for evaluation. Experimental results show that MoltDB achieves 1.3 × transaction throughput and 30% disk I/O latency savings over the state-of-the-art works.