Stablecoins have emerged as a rapidly growing digital payment instrument, raising the question of whether blockchain-based settlement can function as a substitute for incumbent card networks in retail payments. This Systematization of Knowledge (SoK) provides a systematic comparison between stablecoin payment arrangements and card networks by situating both within a unified analytical framework. We first map their respective payment infrastructures, participant roles, and transaction lifecycles, highlighting fundamental differences in how authorization, settlement, and recourse are organized. Building on this mapping, we introduce the CLEAR framework, which evaluates retail payment systems across five dimensions: cost, legality, experience, architecture, and reach. Our analysis shows that stablecoins deliver efficient, continuous, and programmable settlement, often compressing rail-level merchant fees and enabling 24/7 value transfer. However, these advantages are accompanied by an inversion of the traditional pricing and risk-allocation structure. Card networks internalize consumer-side frictions through subsidies, standardized liability rules, and post-transaction recourse, thereby supporting mass-market adoption. Stablecoin arrangements, by contrast, externalize transaction fees, error prevention, and dispute resolution to users, intermediaries, and courts, resulting in weaker consumer protection, higher cognitive burden at the point of interaction, and fragmented acceptance. Accordingly, stablecoins exhibit a conditional comparative advantage in closed-loop environments, cross-border corridors, and high-friction payment contexts, but remain structurally disadvantaged as open-loop retail payment instruments.

Memecoins, emerging from internet culture and community-driven narratives, have rapidly evolved into a unique class of crypto assets. Unlike technology-driven cryptocurrencies, their market dynamics are primarily shaped by viral social media diffusion, celebrity influence, and speculative capital inflows. To capture the distinctive vulnerabilities of these ecosystems, we present the first Memecoin Ecosystem Fragility Framework (ME2F). ME2F formalizes memecoin risks in three dimensions: i) Volatility Dynamics Score capturing persistent and extreme price swings together with spillover from base chains; ii) Whale Dominance Score quantifying ownership concentration among top holders; and iii) Sentiment Amplification Score measuring the impact of attention-driven shocks on market stability. We apply ME2F to representative tokens (over 65% market share) and show that fragility is not evenly distributed across the ecosystem. Politically themed tokens such as TRUMP, MELANIA, and LIBRA concentrate the highest risks, combining volatility, ownership concentration, and sensitivity to sentiment shocks. Established memecoins such as DOGE, SHIB, and PEPE fall into an intermediate range. Benchmark tokens ETH and SOL remain consistently resilient due to deeper liquidity and institutional participation. Our findings provide the first ecosystem-level evidence of memecoin fragility and highlight governance implications for enhancing market resilience in the Web3 era.

Eclipse attacks isolate blockchain nodes by monopolizing their peer-to-peer connections. The attacks were extensively studied in Bitcoin (SP'15, SP'20, CCS'21, SP'23) and Monero (NDSS'25), but their practicality against Ethereum nodes remains underexplored, particularly in the post-Merge settings.

Decentralized Autonomous Organizations (DAOs) signify a groundbreaking approach to Internet-based management, enabled by blockchain technology and cryptocurrencies, and are viewed as fundamental elements of the Web3 ecosystem. In this study, we delve into the concept of DAOs by thoroughly investigating their underlying structure, ideology, and operational principles. Furthermore, we present a novel DAO framework derived from a technical and organizational assessment and provide an overview of cutting-edge DAO tools currently available. This research enables the swift implementation of DAO creation or transformation customized to an organization’s specific stage. Additionally, we recognize current challenges and shortcomings in existing DAOs and propose areas for future exploration.

Recent years have witnessed a surge in deep learning research, marked by the introduction of expensive generative models like OpenAI’s SORA and GPT, Meta AI’s LLAMA series, and Google’s FLAN, BART, and Gemini models. However, the rapid advancement of large models (LM) has intensified the demand for computing resources, particularly GPUs, which are crucial for their parallel processing capabilities. This demand is exacerbated by limited GPU availability due to supply chain delays and monopolistic acquisition by major tech firms. Distributed Machine Learning (DML) methods, such as Federated Learning (FL), mitigate these challenges by partitioning data and models across multiple servers, though implementing optimizations like tensor and pipeline parallelism remains complex. Blockchain technology emerges as a promising solution, ensuring data integrity, scalability, and trust in distributed computing environments, but still lacks guidance on building practical DML systems. In this paper, we propose a trustworthy distributed machine learning (TDML) framework that leverages blockchain to coordinate remote trainers and validate workloads, achieving privacy, transparency, and efficient model training across public remote computing resources. Experimental validation demonstrates TDML’s efficacy in overcoming performance limitations and malicious node detection, positioning it as a robust solution for scalable and secure distributed machine learning.

Dynamic Proactive Secret Sharing (DPSS) is essential for distributed systems, enabling long-term key escrow, BFT protocol reconfiguration, and confidential state machine replication. Yet existing asynchronous schemes, while crucial for realistic settings, suffer from high communication overhead and poor practicality, limiting real-world deployment. We propose PlainDrop, a concise and efficient DPSS protocol designed specifically for asynchronous networks. PlainDrop achieves optimized communication complexity ofO(n2) via commitment–share decoupling combined with homomorphic threshold encryption techniques. PlainDrop also eliminates the need for expensive distributed key generation and complex bivariate polynomial structures by introducing a lightweight silent setup framework and employing direct share processing based on univariate polynomials. We formally prove that PlainDrop provides secrecy, integrity, and termination in asynchronous networks against a mobile adversary corrupting up to one third of the parties. We implement PlainDrop and evaluate it on Amazon EC2 with up to 100 nodes. Our experimental results demonstrate average reductions of 37% and 67% in completion time, and 61% and 89% in communication volume, compared to DyCAPS and LongLive, respectively.

With the growing number of decentralized finance (DeFi) applications, transaction fairness in blockchains has gained much research interest. As a broad concept in distributed systems and blockchains, fairness has been used in different contexts, varying from ones related to the liveness of the system to ones that focus on the received order of transactions. In this work, we revisit the fairness definitions and find that existing fairness definitions are not adapted to blockchains with multiple DApps. We then provide a more generic one calledverifiable fairness. Compared with prior definitions, our notion has two unique features: (i) it relaxes the ordering rules to apredicate; (ii) it enables users to independently verify if their transactions comply with the predicate for concrete applications. We also provide a scheme that achieves verifiable fairness, leveraging trusted hardware. Unlike prior works that usually design a dedicated consensus protocol to achieve fairness, our scheme can be integrated with any blockchain system. Our evaluation results on Amazon EC2 using up to 120 instances across different regions show that our construction imposes only minimal overhead on existing blockchain systems.

Cryptomining poses significant security risks, yet traditional detection methods like blacklists and Deep Packet Inspection (DPI) are often ineffective against encrypted mining traffic and suffer from high false positive rates. In this paper, we propose a practical encrypted cryptomining traffic detection mechanism. It consists of a two-stage detection framework, which can effectively provide fine-grained detection results by machine learning and reduce false positives from classifiers through active probing. Our system achieves an F1-score of 0.99 and identifies specific cryptocurrencies with a 99.39% accuracy rate. Extensive testing across various mining pools confirms the effectiveness of our approach, offering a more precise and reliable solution for identifying cryptomining activities.

Many tracking companies collect user data and sell it to data markets and advertisers. While they claim to protect user privacy by anonymizing the data, our research reveals that significant privacy risks persist even with anonymized data. Attackers can exploit this data to identify users' accounts on other websites and perform targeted identity alignment. In this paper, we propose an effective identity alignment scheme for accurately identifying targeted users. We develop a data collector to obtain the necessary datasets, an algorithm for identity alignment, and, based on this, construct two types of de-anonymization attacks: thepassive attack, which analyzes tracker data to align identities, and theactive attack, which induces users to interact online, leading to higher success rates. Furthermore, we introduce, for the first time, a novel evaluation framework for online tracking-based identity alignment. We investigate the key factors influencing the effectiveness of identity alignment. Additionally, we provide an independent assessment of our generated dataset and present a fully functional system prototype applied to a cryptocurrency use case.

Federated learning (FL) has become a popular approach in Edge AI for extracting valuable knowledge within edge computing (EC) systems. To enhance AI application performance, large-scale models have gained increasing attention due to their strong generalization capabilities. However, training and transmitting such models impose substantial computational and communication overhead on resource-constrained clients at the edge, and exchanging complete models may also compromise model privacy. To alleviate these burdens and safeguard privacy, split learning (SL) has been introduced by combining data and model parallelism. Although SL alleviates resource constraints, it still encounters efficiency and security challenges in EC environments, where heterogeneous clients can slow down training without enhancing accuracy, and malicious clients may manipulate model behavior. To address these challenges, we propose a novel SL framework, CoDefend, which integrates local epoch regulation and time-aware detection. Specifically, local epoch regulation dynamically assigns heterogeneous clients with appropriate local epoch numbers to improve training efficiency, while time-aware detection provides an effective detection window to identify clients' malicious manipulation to improve model security. Moreover, CoDefend jointly optimizes these two strategies by leveraging their interdependence to further improve SL performance. Extensive experiments on both simulated and real-world platforms using NVIDIA Jetson edge nodes demonstrate that CoDefend achieves approximately 2× faster training speed than baseline methods, while maintaining comparable model accuracy and effectively identifying malicious manipulations even under collusion.

As blockchain technology advances, an increasing number of applications require interactions between smart contracts across multiple blockchains. However, existing cross-chain solutions exhibit limited scalability due to heterogeneous blockchain environments and diverse application requirements. A fundamental challenge lies in the absence of a unified resource definition for cross-chain processes, impeding moderate resource allocation and effective conflict resolution. Specifically, when extended to general cross-chain transactions involving invocations among multiple contracts, these methods lack the capability to correctly handle state transitions for all related contracts. This paper proposes AtomXross, a novel cross-chain scheme that supports arbitrary combinations of smart contracts during the cross-chain process. We build a scalable cross-chain architecture based on a relay chain and a cluster of cross-chain nodes to provide better scalability. We propose a unified definition for cross-chain resources within the system and implement an adaptive resource management mechanism on the relay chain, enabling it to record the invocation relationships of contract functions. When a cross-chain transaction involves multiple contract calls, AtomXross can index the calls and generate the corresponding call tree. To address the challenges posed by potential mutual invocations between smart contracts, we design an atomic transaction protocol based on buckle-lock, an ordered two-tier pessimistic locking mechanism. AtomXross ensures that cross-chain transactions do not conflict with each other while remaining compatible with non-cross-chain calls that may occur at any time. Furthermore, we propose a universal programming template for on-chain smart contracts, which enables ordinary smart contracts to acquire cross-chain capabilities. We implement AtomXross based on Hyperledger Fabric and FiscoBCOS. In comparison to WeCross, AtomXross lowers the gas cost on system initialization and incurs only a 14% increase in transaction latency while supporting inter-contract calls.

Model training is increasingly offered as a service for resource-constrained data owners to build customized models. Split Learning (SL) enables such services by offloading training computation under privacy constraints, and evolves towardserverlessandmulti-clientsettings where model segments are distributed across training clients. This cooperative mode assumes partial trust: data owners hide labels and data from trainer clients, while trainer clients produce verifiable training artifacts and ownership proofs. We presentCliCooper, a multi-clientcooperative SL framework tailored for cooperative model training services in heterogeneous and partially trusted environments, where one client contributes data, while others collectively act as SL trainers.CliCooperbridges the privacy and trust gaps through two new designs. First, Differential Privacy–based activation protection and secret label obfuscation safeguard data owners' privacy without degrading model performance. Second, a dynamic chained watermarking scheme cryptographically links training stages on model segments across trainers, ensuring verifiable training integrity, robust model provenance, and copyright protection. Experiments show thatCliCooperpreserves model accuracy while enhancing resilience to privacy and ownership attacks. It reduces the success rate of clustering attacks (which infer label groups from intermediate activation) to 0%, decreases inversion-reconstruction (which recovers training data) similarity from 0.50 to 0.03, and limits model-extraction–based surrogates to about 1% accuracy, comparable to random guessing.

Cloud-hosted large language models (LLMs) have become the de facto planners in agentic systems, coordinating tools and guiding execution over local environments. In many deployments, however, the environment being planned over is private, containing source code, files, credentials, and metadata that cannot be exposed to the cloud. Existing solutions address adjacent concerns, such as execution isolation, access control, or confidential inference, but they do not control what cloud planners observe during planning: within the permitted scope, raw environment state is still exposed. We introduce PLANTWIN, a schema-constrained projection based architecture for cloud-assisted planning that prevents raw local context from leaving the local boundary. The key idea is to project the real environment into a planning-oriented digital twin: a schema-constrained and de-identified abstract graph that preserves planning-relevant structure while removing reconstructable details. The cloud planner operates solely on this sanitized twin through a bounded capability interface, while a local gatekeeper enforces safety policies and cumulative disclo sure budgets. We further formalize the privacy–utility trade-off as a capability granularity problem, define architectural privacy goals using (k,δ)-anonymity and ε-unlinkability, and mitigate compositional leakage through multi-turn disclosure control. We implement PLANTWIN as middleware between local agents and cloud planners and evaluate it on 60 agentic tasks across ten domains with four cloud planners. PLANTWIN achieves SND = 1.0 against passive-observer adversaries, while maintaining planning quality close to full-context systems: three of four cloud planners achieve PQS > 0.79, within ∼4% of the no-privacy Raw Context baseline; the privacy-hardening pipeline stages add less than 2.2 percentage points of further PQS variation. Residual identifiability under stronger structural-fingerprint adversaries persists and is bounded by deployment-side controls rather than architecturally eliminated.

We introduce Split Unlearning, a novel machine unlearning technology designed for Split Learning (SL), enabling the first-ever implementation of Sharded, Isolated, Sliced, and Aggregated (SISA) unlearning in SL frameworks. Particularly, the tight coupling between clients and the server in existing SL frameworks results in frequent bidirectional data flows and iterative training across all clients, violating the ''Isolated'' principle and making them struggle to implement SISA for independent and efficient unlearning. To address this, we propose SplitWiper with a new one-way-one-off propagation scheme, which leverages the inherently ''Sharded'' structure of SL and decouples neural signal propagation between clients and the server, enabling effective SISA unlearning even in scenarios with absent clients. We further design SplitWiper+ to enhance client label privacy, which integrates differential privacy and label expansion strategy to defend the privacy of client labels against the server and other potential adversaries. Experiments across diverse data distributions and tasks demonstrate that SplitWiper achieves 0% accuracy for unlearned labels, and 8% better accuracy for retained labels than non-SISA unlearning in SL. Moreover, the one-way-one-off propagation maintains constant overhead, reducing computational and communication costs by 99%. SplitWiper+ preserves 90% of label privacy when sharing masked labels with the server.

Trust management systems (TMS) are crucial for managing trust in distributed environments. The rise of decentralized systems and blockchain has sparked interest in credential-based decentralized trust management systems (DTMS). This paper bridges the gap between theory and practice through a systematic review of credential-based DTMS. We analyze existing DTMS solutions through multiple dimensions, including their architectural designs, credential mechanisms, and trust evaluation models. Our survey provides a detailed taxonomy of credential-based DTMS approaches and establishes comprehensive evaluation criteria for assessing DTMS implementations. Through extensive analysis of current systems and implementations, we identify critical challenges and promising research directions in the field. Our examination offers valuable insights for researchers and practitioners working on DTMS, particularly in areas such as access control, reputation systems, and blockchain-based trust frameworks.

Decentralized communication is becoming an important use case within Web3. On Ethereum, users can repurpose the transaction input data field to embed natural-language messages, commonly known as Input Data Messages (IDMs). However, as IDMs gain wider adoption, there has been a growing volume of toxic content on-chain. This trend is concerning, as Ethereum provides no protocol-level support for content moderation.We propose two moderation frameworks for Ethereum IDMs: (i) BUILDERMOD, where builders perform semantic checks during block construction; and (ii) USERMOD, where users proactively obtain moderation proofs from external classifiers and embed them in transactions. Our evaluation reveals that BUILDERMOD incurs high block-time overhead, which limits its practicality. In contrast, USERMOD enables lower-latency validation and scales more effectively, making it a more practical approach in moderation-aware Ethereum environments.Our study lays the groundwork for protocol-level content governance in decentralized systems, and we hope it contributes to the development of a decentralized communication environment that is safe, trustworthy, and socially responsible.

Unmanned Aerial Vehicle (UAV) flight control systems are increasingly exposed to software-level security threats. However, existing Trusted Execution Environment (TEE) technologies that can effectively defend against software attacks are difficult to deploy on the low-cost UAVs due to: (1) the lack of onboard security hardware, (2) limited Memory Protection Unit (MPU) resources, and (3) strict real-time requirements. To address these issues, we design FC-TEE, a lightweight TEE framework tailored for low-cost UAV platforms, which integrates fine-grained memory isolation and a multi-level task scheduling strategy. By analyzing the flight control code and control principles, we provide three key attributes (task priority, maximum invocation frequency, and required argument types) of flight control tasks and classify these tasks into two categories (privileged tasks and common tasks) based on the attributes. Then, we run the privileged tasks in FC-TEE to achieve memory isolation from common tasks, protecting the privileged tasks from software attacks. Meanwhile, we design multi-level task scheduling based on the task priority and categories to ensure the real-time requirements of the flight control system. Compared with existing UAV protection solutions such as MINION and TrustZone-based RT-TEE, the average additional execution overhead introduced by FC-TEE only is 2.7%. We prototype FC-TEE on a real quadrotor platform and validate its effectiveness through task-level performance and security evaluations.

Bitcoin Request for Comment 20 (BRC-20) token mania was a key storyline in the middle of 2023. Setting it apart from conventional Ethereum request for comments (ERC)-20 token standards on Ethereum, BRC-20 introduces nonfungibility to Bitcoin through an editable field in each satoshi (0.00000001 Bitcoin, the smallest unit), making them unique. In this article, we pioneer the exploration of this concept, covering its intricate mechanisms, features, and state-of-the-art applications. By analyzing the multidimensional data spanning over months with factual investigations, we conservatively comment that while BRC-20 expands Bitcoin’s functionality and applicability, it may still not match Ethereum’s abundance of decentralized applications and similar ecosystems.

As a typical instance of human–computer interaction, the notion of decentralized autonomous organization (DAO) represents an organization constructed by automatically executed rules, such as via smart contracts, incorporating features of the permissionless committee, transparent proposals, and fair contributions by stakeholders. As of May 2023, DAO has impacted over $24.3B market caps. However, there are limited studies focused on this emerging field. To fill the gap, we start from the ground truth by empirically studying the breadth and depth of the DAO markets in mainstream public chain ecosystems in this article. We dive into the most widely adoptable DAO launchpad,Snapshot, which covers 95% of the wild DAO projects for data collection and analysis. By integrating extensively enrolled DAOs and corresponding data measurements, we explore statistical resources from Snapshot and analyze data from 581 DAO projects, encompassing 16 246 proposals over the course of 3+ years. Our empirical research has uncovered a multitude of previously unknown facts about DAOs, spanning topics such as their status, features, performance, threats, and ways of improvement. We have distilled these findings into a series of key insights and takeaway messages, emphasizing their significance. Notably, our study is the first of its kind to comprehensively examine the DAO ecosystem with a focus on scale and scope of data, real-time relevance, practical implementations, and comprehensive metrics, addressing critical gaps in the current literature.

With the increasing scale and complexity of online activities, accountability, as an after-the-fact mechanism, has become an effective complementary approach to ensure system security. Decades of research have delved into the connotation of accountability. They fail, however, to achieve practical accountability of decryption. This paper seeks to address this gap. We consider the scenario where a client (called encryptor, her) encrypts her data and then chooses a delegate (a.k.a. decryptor, him) that stores data for her. If the decryptor initiates an illegitimate decryption on the encrypted data, there is a non-negligible probability that this behavior will be detected, thereby holding the decryptor accountable for his decryption. We make three contributions. First, we review key definitions of accountability known so far. Based on extensive investigations, we formalize new definitions of accountability specifically targeting the decryption process, denoted as accountable decryption, and discuss the (im)possibilities when capturing this concept. We also define the security goals in correspondence. Second, we present a novel Trusted Execution Environment(TEE)-assisted solution aligning with definitions. Instead of fully trusting TEE, we take a further step, making TEE work in the “trust, but verify” model where we trust TEE and use its service, but empower users (i.e., decryptors) to detect the potentially compromised state of TEEs. Third, we implement a full-fledged system and conduct a series of evaluations. The results demonstrate that our solution is efficient. Even in a scenario involving$300,000$log entries, the decryption process concludes in approximately 5.5ms, and malicious decryptors can be identified within 69ms.

Trusted Execution Environment (TEE)-assisted confidential smart contracts (TCSC) have attracted extensive attention from both academia and industry. Despite an enormous number of TCSC projects, the extent of confidentiality offered by them remains being questioned: the factual and fictional aspects are not well distinguished, which limits their adoption. In this paper, we provide a formal treatment of TCSC, endowing them with an expressive syntax and security definitions. Based on these definitions, we propose a provably secure TCSC instantiation. Then, we investigate each algorithm and identify the implementation flaws that may make a TCSC system violate its security properties. Our analysis reveals the gap between theoretical security models and real-world implementations: even assuming a TCSC is provably secure by design, it may still fail in practice. We further compare our TCSC instantiation with 16 representative TCSC systems. Our results show that, surprisingly, all these surveyed projects are subject to practical attacks. Finally, we implement a TCSC prototype and conduct a comprehensive evaluation, revealing the overheads of distributed key management and the performance challenges of executing complex contracts within TEEs.

Random beacons are of paramount importance in distributed systems (e.g., blockchain, electronic voting, governance). The sheer scale of nodes inherent in distributed environments necessitates minimizing communication overhead per node while ensuring protocol availability, particularly under adversarial conditions. Existing solutions have managed to reduce the optimistic overhead to a minimum ofO(n2), wherenrepresents the node count of the system. In this paper, we step further by proposing and implementing RandFlash, a leaderless random beacon protocol that achieves an optimistic communication complexity ofO(nlogn). Evaluation results demonstrate that RandFlash outperforms existing constructions, RandPiper (CCS’21) and OptRand (NDSS’23), in terms of the number of random beacons generated within largescale networks comprising 64 nodes or more (e.g., in sizes of 80 and 128). Furthermore, RandFlash exhibits resilience, capable of withstanding up to one-third of the nodes acting maliciously, all without the need for strongly trusted setups (i.e., embedding a secret trapdoor by trusted third parties). We also provide formal security proofs validating all properties upheld by this lineage.

Federated learning (FL) offers an effective learning architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, overdependence on a central coordinator, and lack of an open and fair incentive mechanism collectively hinder FL's further development. We propose IronForge, a new generation of FL framework, that features a directed acyclic graph (DAG)-based structure, where nodes represent uploaded models, and referencing relationships between models form the DAG that guides the aggregation process. This design eliminates the need for central coordinators to achieve fully decentralized operations. IronForge runs in a public and open network and launches a fair incentive mechanism by enabling state consistency in the DAG. Hence, the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of IronForge. Experimental results based on a newly developed test bed FLSim highlight the superiority of IronForge to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, IronForge is the first secure and fully decentralized FL (DFL) framework that can be applied in open networks with realistic network and training settings.

As Artificial Intelligence (AI) integrates into diverse areas, particularly in content generation, ensuring rightful ownership and ethical use becomes paramount, AI service providers are expected to prioritize responsibly sourcing training data and obtaining licenses from data owners. However, existing studies primarily center on safeguarding static copyrights, which simply treat metadata/datasets as non-fungible items with transferable/trading capabilities, neglecting the dynamic nature of training procedures that can shape an ongoing trajectory. In this paper, we presentIBis, a blockchain-based framework tailored for AI model training workflows. Our design can dynamically manage copyright compliance and data provenance in decentralized AI model training processes, ensuring that intellectual property rights are respected throughout iterative model enhancements and licensing updates. Technically,IBisintegrates on-chain registries for datasets, licenses and models, alongside off-chain signing services to facilitate collaboration among multiple participants. Further,IBisprovides APIs designed for seamless integration with existing contract management software, minimizing disruptions to established model training processes. We implementIBisusing Daml on the Canton blockchain. Evaluation results showcase the feasibility and scalability ofIBisacross varying numbers of users, datasets, models, and licenses.

Asynchronous common subset (ACS) has been extensively studied since the asynchronous Byzantine fault tolerance (BFT) framework was introduced by Ben-Or, Kemler, and Rabin (BKR). The line of work (i.e., HoneyBadgerBFT, BEAT, EPIC) uses parallel reliable broadcast (RBC) and asynchronous binary agreement (ABA) instances to reach an agreement on a subset of proposed transactions.In this paper, we further progress the BKR paradigm by presenting Janus, the first hybrid ACS protocol leveraging trusted hardware components. Janus is the first ACS protocol that tolerates a minority of Byzantine processes and that has $\mathcal{O}\left( {{n^2}} \right)$ message complexity. Supported by trusted hardware components, we introduce a provable broadcast primitive to replace RBC, and develop a resilient binary agreement protocol. Messages for concurrent instances of agreement are aggregated into vectors. Our experimental results demonstrate significant performance improvements over predominant ACS constructions with a 92%+ increase compared to HoneyBadgerBFT and a 47%+ increase compared to BEAT. Additionally, we provide a comparison with open-source hybrid BFT protocols that operate under a partially synchronous network, highlighting the performance enhancement compared to previous hybrid protocols that also tolerate the Byzantine minority (e.g., MinBFT and Damysus, by 49%+).

In this paper, we design, implement, and (partially-) evaluate a lightweight bridge (as a type of middleware) to connect the Bitcoin and Ethereum networks that were heterogeneously uncontactable before. Inspired by the recently introduced Bitcoin Request Comment (BRC-20) standard, we leverage the flexibility of Bitcoin inscriptions by embedding editable operations within each satoshi and mapping them to programmable Ethereum smart contracts. A user can initialize his/her requests from the Bitcoin network, subsequently triggering corresponding actions on the Ethereum network. We validate the lightweight nature of our solution and its ability to facilitate secure and seamless interactions between two heterogeneous ecosystems.

This paper presents a primary exploration of Bitcoin inscriptions. We dive into the technological underpinnings and offer a detailed comparative analysis between Bitcoin inscriptions and NFTs on other blockchains. Further, we explore a wide range of use cases and significant opportunities for future innovation, including inscription derivative protocols, Bitcoin Layer2 solutions, and interoperability techniques.

Staking has emerged as a crucial concept following Ethereum’s transition to Proof-of-Stake consensus. The introduction of Liquid Staking Derivatives (LSDs) has effectively addressed the illiquidity issue associated with solo staking, gaining significant market attention. This paper analyzes the LSD market dynamics from the perspectives of both liquidity takers (LTs) and liquidity providers (LPs). We first quantify the price discrepancy between the LSD primary and secondary markets. Then we investigate and empirically measure how LTs can leverage such discrepancy to exploit arbitrage opportunities, unveiling the potential barriers to LSD arbitrages. In addition, we evaluate the financial profit and losses experienced by LPs who supply LSDs for liquidity provision. Our results show that 66% of LSD liquidity positions generate returns lower than those from simply holding the corresponding LSDs.

In this paper, we propose aggregated vector consensus, a new vector consensus primitive designed for asynchronous networks. The primitive achieves agreement by outputting a vector of values aggregated from independent process inputs. We then introduce Juno, an asynchronous common subset (ACS) protocol that fully implements our aggregated vector consensus to attain optimal ${\mathcal{O}}\left({{n^2}}\right)$ message complexity.We further implement and evaluate Juno in comparison with the legacy HoneyBadgerBFT and the state-of-the-art Dory. Experiment results demonstrate its efficacy and efficiency. Our protocol demonstrates an average throughput performance improvement of 93% compared with HoneyBadgerBFT and a 47% improvement compared with Dory. Notably, our study makes significant progress in addressing the gap in applying vector consensus protocol in fully asynchronous networks.

In this article, we explore the aftermath of the Silicon Valley Bank (SVB) collapse, with a particular focus on its impact on crypto markets. We conduct a multidimensional investigation, which includes a factual summary, analysis of user sentiment, and examination of market performance. We uncover a somewhat counterintuitive finding: the SVB collapse did not lead to the destruction of cryptocurrencies; instead, they displayed resilience.

Web3 is leading a wave of the next generation of web services that even many Web2 applications are keen to ride. However, the lack of Web3 background for Web2 developers hinders easy and effective access and transition. On the other hand, Web3 applications desire encouragement and advertisement from conventional Web2 companies and projects due to their low market shares. In this article, we propose a seamless transition framework that transits Web2 to Web3, named WEBTTCOM [WEBTTCOM stands for Web2 (two)–Web3 (three) Communicator], after exploring the connotation of Web3 and the key differences betweenWeb2 andWeb3 applications.We also provide a full-stack implementation as a use case to support the proposed framework, followed by performance evaluation and surveys with ~1000 participants that show ~80% positive and ~20% neutral responses. We confirm that the proposed framework WEBTTCOM addresses the defined research question, and the implementation well satisfies the framework WEBTTCOM in terms of strong necessity,usability, andcompletenessbased on the survey results.

The traditional decentralised supply chains are facing challenges to meet the increasing demands for openness, transparency, trust and efficiency. As a result, blockchain-based decentralised supply chains are emerging. However, how to identify and discover various services in a decentralised supply chain remains an unsolved issue. This paper presents a new framework for modelling and discovering services in decentralised supply chain systems. In the framework, W3C Decentralised Identifier (DID) is adopted to describe service attributes to meet different business and technical requirements for a supply chain service. While blockchain is used for publishing and storing DID, a graphic-database-based service repository is proposed to organize these DID strings in a natural way as a graph for better service discovery. An Ethereum-based prototype is implemented as a proof of concept to demonstrate its feasibility and usefulness.

Algorithmic stablecoins (AS) are one special type of stablecoins that are not backed by any asset. They stand to revolutionize the way a sovereign flat operates. As implemented, AS are poorly stabilized in most cases; their prices easily deviating from the target or even falling into a catastrophic collapse, and are as a result often dismissed as a Ponzi scheme. However, what is the essence of Ponzi? In this paper, we try to clarify such a deceptive concept and reveal how AS work from a higher level. We find that Ponzi is basically a financial protocol that pays existing investors with funds collected from new ones. Running a Ponzi, however, does not necessarily imply that any participant is in any sense losing out, as long as the game can be perpetually rolled over. Economists call such realization as a rational Ponzi game. We thereby propose a rational model in the context of AS and draw its holding conditions. We apply the model to examine: whether or not the algorithmic stablecoin is a rational Ponzi game. Accordingly, we discuss two types of algorithmic stablecoins (Rebase & Seigniorage Shares) and dig into the historical market performance of a number of impactful projects to demonstrate the effectiveness of our model.

Across industries, there is an ever-increasing rate of data sharing for collaboration and innovation between organizations and their customers, partners, suppliers, and internal teams. However, many enterprises are restricted from freely sharing data due to regulatory restrictions across different regions, performance issues in moving large volume data, or requirements to maintain autonomy. In such situations, the enterprise can benefit from the concept of federated learning, in which machine learning models are constructed at various geographic sites. In this paper, we introduce a general framework, namely BDSP, to share data among enterprises based on Blockchain and federated learning techniques. Specifically, we propose a transparency contribution accounting mechanism to estimate the valuation of data and implement a proof-of-concept for further evaluation. The extensive experimental results show that the proposed BDSP has a competitive performance with higher training accuracy, an increase of over 5%, and lower communication overhead, reducing 3 times, compared to baseline approaches.

Existing NFTs confront restrictions of one-time incentive and product isolation. Creators cannot obtain benefits once having sold their NFT products due to the lack of relationships across different NFTs, which results in controversial profit sharing. This paper proposes a referable NFT solution to extend the incentive sustainability of NFTs. We construct the referable NFT (rNFT) network to increase exposure and enhance the referring relationship of inclusive items. We introduce the DAG topology to generate directed edges between each pair of NFTs with corresponding weights and labels for advanced usage. We accordingly implement and propose the scheme under Ethereum Improvement Proposal (EIP) standards, indexed in EIP-5521. Further, we provide the mathematical formation to analyze the utility for each rNFT participant. The discussion gives general guidance among multi-dimensional parameters. The solution, as a result, shape the recognition of potential values hidden in isolated NFTs and raise the interest of communities toward the discovery of NFT derivatives. To our knowledge, this is the first study to build a referable NFT network, explicitly showing the virtual connections among NFTs.

Decentralized autonomous organizations (DAOs) are critical to the blockchain ecosystem as they enable decentralized decision-making and governance, and facilitate the creation of decentralized applications (DApps) and organizations. However, despite significant importance, there is currently a lack of a comprehensive overview and detailed understanding of DAOs. To address the gap, this work presents a primary investigation of DAOs (35+). We category, examine and evaluate existing DAOs regarding their operational features, (non-)functionalities and real-world performance. In addition, we provide a consolidated exploration of DAOs by conducting a literature review [1] and an empirical study on mainstream projects, particularly Snapshot [2]. Our research contributes to a better understanding of DAOs and their potential impact on the blockchain ecosystem.

The development of effective recommender systems for Web3 assets, such as the Non-Fungible Token (NFT), requires concentration along with the growth of popularity and heterogeneity in many potential applications such as Web3 gaming and NFT rental markets, the requirements of predicting rNFT classification desire a practical solution. In this paper, we make use of the referable NFT (rNFT11In this work, rNFT mainly refers to the EIP-5521 protocol and corresponding formed network/topology [1], while NFT is used in the context of a single node, node sets, or products that align with the EIP-5521 protocol.) standard [2], indexed EIP-5521, to construct an rNFT classification framework leveraging Graph Neural Network (GNN), an emerging branch of Deep Learning (DL), which learns on the inherent topology of graph-based data. In particular, we first transform the rNFT backward and onward reference relationship to a Direct Acyclic Graph (DAG) and model appropriate node and edge features from rNFT metadata and associated token transactions. Next, a multi-layer GraphSage model is designed to include the collected features for the learning process. In this way, the model takes into account graph topology together with features to classify both the existing and incoming NFT nodes in a supervised way. We also give comprehensive elaboration on the architecture of the new GNN-based recommender system with discussions in regard to its characteristics and challenges. Furthermore, we expect to conduct extensive experiments, by presenting an initial plan, to show the feasibility and efficacy of our system.

Architectural design contexts contain a set of factors that greatly influence software application development. Among them, organizational design contexts consist of high-level company concerns and how it is structured, for example, stakeholders and development schedules heavily impacting design considerations. The Decentralized Autonomous Organization (DAO), as a vital concept in the Web3 space, represents an organization constructed by automatically executed rules, such as via smart contracts, holding features of the permissionless committee, transparent proposals, and fair contribution by participated stakeholders. In this work, we conduct a systematic literature review of existing DAO literature to summarize its structural features, benefits and challenges, and potential development directions in the context of Web3 applications.

As blockchain-based commercial projects and startups flourish, efficiency becomes one of the critical metrics in designing blockchain systems. Due to its high efficiency, Proof of Authority (PoA) Aura has become one of the most widely adopted consensus solutions for blockchains. Our research finds over 4,000 projects have used Aura and its variants. In this paper, we provide a rigorous analysis of Aura. We propose three types of time-manipulation attacks, where a malicious leader simply needs to modify the timestamp in its proposed block or delay it to extract extra benefits. These attacks can easily break the legal leader election, thus directly harming the fairness of the block proposal. We apply our attacks to a mature Aura project called OpenEthereum. By repeatedly conducting our attacks1 over 15 days, we find that an adversary can gain on average 200% mining rewards of their fair shares. Furthermore, such attacks can even indirectly break the finality of blocks and the safety of the system. Based on the deployment of Aura as of September 2022, the potentially affected market cap is up to 2.13 billion USD. As a by-product, we further discuss solutions to mitigate such issues and report our observations to official teams.

NEO is one of the top public chains worldwide. It adopts a new consensus algorithm calleddelegated Byzantine Fault Tolerance(dBFT). In this article, we formalize dBFT via the state machine replication model and point out its potential issues. Our theoretical analysis indicates that dBFT could guarantee neitherlivenessnorsafety, even if the number of Byzantine nodes is no more than the threshold, which has contradicted the established security claim. Then, we identify two attacks and successfully simulate them. Finally, we provide recommendations. Notably, NEO official team has accepted our suggested fixes.

Garg et al. (TCC 2018) defined the notion of registration-based encryption (RBE) where the private key generator (PKG) is decoupled from key management and replaced by a key curator (KC). KC does not possess any cryptographic secrets and only plays the role of aggregating the public keys of all the registered users and updating the public parameters whenever a new user joins the system, which solves the key escrow issue. Notwithstanding, RBE still places a significant amount of trust in KC, whose actions are not accountable, e.g., it could secretly register multiple keys for already registered users. In this article, we propose a blockchain-based RBE framework, which provides total transparency and decentralization of KC by leveraging smart contracts. Our framework transfers the right of key management from KC to individual participants and keeps publicly upgradable parameters on-chain. We provide a basic construction that calculates the public parameter on-chain and an extended construction with better efficiency, which merely calculates the roots of trees on-chain. Our basic version is theoretically feasible, while the extended version is practically feasible. In particular, the enhanced scheme reduces computing complexity to a constant level. Our prototype implementation and evaluation results demonstrate that our extended construction is satisfactorily efficient.

Proof of Authority (PoA) is a type of permissioned consensus algorithm with a fixed committee. PoA has been widely adopted by communities and industries due to its better performance and faster finality. In this paper, we explore the unfairness issue existing in the current PoA implementations. We have investigated 2,500+ in the wild projects and selected 10+ as our main focus (covering Ethereum, Binance smart chain, etc.). We have identified two types of order manipulation attacks to separately break the transaction-level (a.k.a. transaction ordering) and the block-level (sealer position ordering) fairness. Both of them merely rely on honest-but-profitable sealer assumption without modifying original settings. We launch these attacks on the forked branches under an isolated environment and carefully evaluate the attacking scope towards different implementations. To date (as of Nov 2021), the potentially affected PoA market cap can reach up to 681,087 million USD. Besides, we further dive into the source code of selected projects, and accordingly, propose our recommendation for the fix. To the best of knowledge, this work provides the first exploration of the unfairness issue in PoA algorithms.

In this paper, we propose a frontrunning block attack against the Clique-based Proof of Authority (PoA) algorithms. Our attack can frontrun blocks from honest in-turn sealers by breaking the leader rotation’s proper order. By falsifying the priority parameters (both difficulty and delay time), a malicious non-in-turn sealer can always successfully occupy the leader position and produce advantageous blocks that may contain profitable transactions. As a typical instance, we apply our attack to a mature Clique-based project, HPB (with the market cap $10,128,116, as of Jan 2022). Experimental results demonstrate the effectiveness and feasibility. Then, we further propose fixes by checking sealer’s identity. Our investigation and suggestion have been submitted to its official team. We believe this work can act as, at least, a warning case for Clique variants to avoid repeating such design mistakes.

Difficulty adjustment algorithm (DAA) is critical for PoW blockchain systems in currency issuance, transaction throughput, and blockchain security. Most DAAs concentrate on the unique goal of block-making stability from seconds to minutes. In contrast, we propose a new DAA with the goal of stabilizing orphan rates among the blocks made recently, observing that the orphan rate is more indicative since too high orphan rate indicates poor blockchain security and waste of honest hash power whereas too low orphan rate indicates poor blockchain throughout and wast of time awaiting other blocks. We notice a security defect of the recent orphan-rate DAA (NDSS 2022) in which an attacker may continually create orphan blocks and mislead miners to wrongly increase the difficulty. To fix this defect, our enhanced DAA takes into the orphan rate variation during the historical slots with smooth exponential decay affection on current difficulty adjustment. Theoretical and experimental analyses show that our DAA is effective and secure.

As one of the most frequently used Internet-of-Things (IoT) devices, energy smart meter has been widely adopted to facilitate the measures of residential energy use. Residents pay for the bills from energy suppliers according to their monthly/seasonal usage. Practically, there is a demand from residents/governments to check whether the bills are in line with their real consumptions. However, it is challenging to realize this demand due to two critical problems. The first problem refers to the nonrepudiated privacy issue caused by access to residents’ energy consumption history (e.g., data integrity may be questioned, and residents’ daily timetables may be exposed). The second problem comes from the efficiency requirement for bulk auditing requests on residents’ bills and consumptions, usually risen by governments. So far, we have not found any solutions that can be directly used in this case. In this article, we propose using homomorphic encryption cooperated with the blockchain technique to leverage the data auditing and privacy-preserving requirements. We also employe a certificateless signature to resolve the efficiency bottleneck in batch auditing. This framework, calledpAuditChain, not only accepts personal requests from residents for consumption checking but also handles bulk auditing requests issued by governments. To validate the correctness of the framework functions, we carried out a series of theoretical analysis, especially on the privacy preserving and auditing processes. To the best of our knowledge, the proposed framework is among the first solutions to improve the security and privacy of bills without losing the auditing function. Our approach concerns with IoT smart meters in energy supply industries and could be further extended to other forms of IoT devices with the bill demands.

The blockchain-based smart contract lacks privacy, since the contract state and instruction code are exposed to the public. Combining smart-contract execution with Trusted Execution Environments provides an efficient solution, called TEE-assisted smart contracts (TCSC), for protecting the confidentiality of contract states. However, the combination approaches are varied, and a systematic study is absent. Newly released systems may fail to draw upon the experience learned from existing protocols, such as repeating known design mistakes or applying TEE technology in insecure ways. In this paper, we first investigate and categorize existing systems into two types: the layer-one solution and the layer-two solution. Then, we establish an analysis framework to capture their common aspects, covering desired properties (for contract services), threat models, and security considerations (for underlying systems). Based on our taxonomy, we identify their ideal functionalities, and uncover fundamental flaws and challenges in each specification’s design. We believe that this work would provide a guide for the development of TEE-assisted smart contracts, as well as a framework to evaluate future TCSC systems.

A large number of consensus algorithms have been proposed. However, the requirement of strict consistency limits their wide adoption, especially in high-performance required systems. In this paper, we propose a weak consensus algorithm that only maintains the consistency of relative positions between the messages. We apply this consensus algorithm to construct a high-performance blockchain system, called Sphinx. We implement the system with 32k+ lines of code including all components like consensus/P2P/ledger/etc. The evaluations show that Sphinx can reach a peak throughput of 43k TPS (with 8 full nodes), which is significantly faster than current blockchain systems such as Ethereum given the same experimental environment. To the best of our knowledge, we present the first weak consensus algorithm with a fully implemented blockchain system.

In this paper, we focus on the security of outsourced database.A verification scheme for outsourced database is proposed based on the verifiable polynomial technique.In this scheme, we consider the encrypted database.The outsourced high degree polynomial will enhance the authenticity of the data.If the cloud server returns fake data, it will be detected by the clients easily.