Decentralized marketplaces in Web3 aim to protect against censorship, bias, and single points of failure that may exist in their centralized counterparts. Still, some mechanisms tend to remain centralized, for example the search mechanism that enables discovery of new assets in the market. Such vulnerabilities have been exploited in live marketplaces in recent years: it is all the more essential to provide protection mechanisms. In this thesis, we propose protocols to uphold the reliability and fairness of marketplace mechanisms, notably through resilience against colluding malicious actors. First, to address decentralized selection of a subset of participants among a population comprising malicious actors, we contribute a blockchain-based protocol to avoid malicious actors swaying selection to their benefit. Then, considering selected sets of participants that will work together on tasks in a decentralized computing marketplace, in an environment with no access to trustworthy or non-confidential monitoring information, we present an incentive mechanism that collectively punishes or rewards task participants based on the outcome of their tasks. We also describe and evaluate how to meet a target success rate for the marketplace's tasks: our proposed algorithm is able to meet such targets and to reduce the failure rate by 5 to 10 times compared to an unprotected system. Additionally, we show how providers of a marketplace's search mechanism can favor a subset of search consumers, granting them an unfair advantage in accessing information about the most recent state of the market. We protect decentralized marketplaces' search with our protocol COoL-TEE, which enables honest search consumers to avoid malicious search providers, who selectively delay responses to benefit colluding consumers. Honest consumers collaborate with Trusted Execution Environments (TEEs) inside the host providers, in order to select close, fast, and honest providers. Using simulations of consumers sending search requests from around the globe to geo-distributed providers hosted in datacenters, we illustrate how COoL-TEE reduces malicious advantage close to a scenario without attacks. Finally, many TEE and traditional protocols rely on trustworthy time measurements for their execution logic, including COoL-TEE. However, attackers controlling the operating system are capable of attacking the TEE's time perception and, in turn, of manipulating the protocols depending on the timestamps. We contribute a public implementation of the state-of-the-art but closed-source protocol Triad and empirically showcase attacks. Calibration can be manipulated to affect the TEE's perceived clock speed. Furthermore, attacks on a compromised machine could propagate to honest machines participating in Triad's trusted time protocol. We discuss mitigations to these vulnerabilities for higher resilience against such attacks.
The Intelligent System of Emergent Knowledge (ISEK) establishes a decentralized network where human and artificial intelligence agents collaborate as peers, forming a self-organizing cognitive ecosystem. Built on Web3 infrastructure, ISEK combines three fundamental principles: (1) a decentralized multi-agent architecture resistant to censorship, (2) symbiotic AI-human collaboration with equal participation rights, and (3) resilient self-adaptation through distributed consensus mechanisms. The system implements an innovative coordination protocol featuring a six-phase workflow (Publish, Discover, Recruit, Execute, Settle, Feedback) for dynamic task allocation, supported by robust fault tolerance and a multidimensional reputation system. Economic incentives are governed by the native $ISEK token, facilitating micropayments, governance participation, and reputation tracking, while agent sovereignty is maintained through NFT-based identity management. This synthesis of blockchain technology, artificial intelligence, and incentive engineering creates an infrastructure that actively facilitates emergent intelligence. ISEK represents a paradigm shift from conventional platforms, enabling the organic development of large-scale, decentralized cognitive systems where autonomous agents collectively evolve beyond centralized constraints.
Blockchain's economic value lies in enabling financial and economic transactions without relying on trusted, centralized intermediaries. In practice, however, transactions pass through a fragmented chain of intermediaries before being included on-chain. Because standard blockchain data reveal only the winning block, this process is largely unobservable. We address this limitation by constructing a novel dataset of 15,097 non-winning Ethereum blocks, that is, blocks proposed but not selected for inclusion. We show that 21% of user transactions are delayed: they appear in candidate blocks but not in the winning block, implying that fragmented routing materially affects inclusion time. We further show that execution quality varies substantially across candidate blocks: for the same swap, both execution probability and execution price differ across proposed blocks. To study these differences, we examine competition between two arbitrage bots trading between decentralized and centralized exchanges. We find that, conditional on inclusion in a block that also contains transactions from these bots, user swaps in the same (opposite) direction are less likely (more likely) to execute and receive worse (better) prices. These results show that routing and block composition are central determinants of execution quality and market quality in on-chain markets.
Zhaowei Hu, Jin Rui-fang, HangYi Quan, Shiyun Ni · 5 authors
To address privacy leakage risks arising from low collaborative user engagement, third-party trust deficits, and insufficient collaboration timeliness in location-based services (LBS), this paper proposes a dual-protection framework integrating blockchain technology and threshold cryptography for safeguarding location privacy. The framework employs asymmetric encryption with Shamir's (t, n) secret sharing to encrypt user queries, distributing decryption key fragments to collaborative users while generating n anonymous service requests through location generalization strategies. A temporary private blockchain constructed using smart contracts ensures confidential data transmission, supported by a dynamic privacy parameter configuration system based on Byzantine fault tolerance. The framework implements a priority-response consensus mechanism through Token-based equity proof-of-stake, prioritizing service for users with higher Token values. To mitigate privacy breaches caused by unresponsive collaborators, a competitive incentive mechanism ensures timely information submission. Through ciphertext fragment verification algorithms and Lagrange interpolation-based key reconstruction, the framework enables secure query decryption and service matching in untrusted third-party environments, guaranteeing information security, integrity, and non-repudiation. Experimental validation using real-world datasets confirms the framework's feasibility and operational effectiveness.
Mai Shawkat, Ali El-desoky, Zainab H. Ali, Mofreh Salem
Abstract The Industrial Internet of Things (IIoT) applications have been recognized as an advancement of the conventional wireless network that concentrates on incorporating processes and machines specifically for industrial applications. These Industrial applications frequently use centralized machine learning (ML) approaches not only to enhance their functionality but also to evaluate sensor data for a variety of purposes, including digitizing operations in manufacturers, forecasting maintenance requirements in industrial equipment, and detecting anomalies for security monitoring, they may adversely affect overall system performance due to high cost of computing power and privacy concerns, as so much data is stored on a cloud server. Federated Learning (FL) has emerged as a new benchmark for centralized ML methods. It sends models to user devices without transferring private data to third-party or central servers; it is one of the promising solutions to data leakage issues. This work introduces a comprehensive overview of the advancements, challenges, and future directions in FL adoption with edge devices. It covers security threats and mitigation strategies, emphasizing its categories, privacy and concerns, communication overhead obstacles, heterogeneity issues, aggregation techniques, and associated development tools. This review paper delves into FL-related topics, including system platforms, offering a comprehensive overview of best practice systems in real-world FL applications. To ensure security in IIoT applications, reviewing threats and mitigation strategies by integrating FL with state-of-the-art technologies such as blockchain, federated reinforcement learning, and federated meta-learning has been explored. Finally, the recent research is taking place to determine new future directions and opportunities for FL security defense mechanisms has been considered at the end of this review paper.
Purpose The construction industry, contributing approximately 39% of global carbon emissions, faces challenges to reach net-zero emissions by 2050. Traditional methods for estimating and managing carbon emissions suffer from inaccuracies, low transparency and data integrity issues, highlighting the need for trustworthy and efficient solutions. This paper aims to demonstrate how blockchains can enhance the accuracy of tracking carbon emissions and streamlining carbon trading, providing a robust system to manage and reduce carbon emissions effectively. Design/methodology/approach A case study-based approach is adopted to develop a blockchain-based system (EcoConstruct) to track carbon emissions and circularity of construction materials and facilitate carbon trading in the industry. The implementation uses smart contract technology and the Beneficial Assets Ownership protocol in the Tezos blockchain to validate carbon emission tracking, carbon trading and circularity criteria. The system was evaluated and validated through expert feedback, ensuring its practical applicability and effectiveness. Findings EcoConstruct demonstrates advancements in transparency, data integrity and efficiency in carbon estimation and trading. The system’s immutable ledger securely stores carbon emissions and their compensations using non-fungible tokens called carbon rewards. This system facilitates transparent and accountable carbon trading among stakeholders (clients, contractors and material suppliers). The findings highlight the potential of blockchains to overcome current challenges in carbon emissions management and trading in the construction industry. Originality/value EcoConstruct provides a novel blockchain-based solution for managing carbon emissions and promoting sustainability in construction, moving beyond conceptualisation by leveraging blockchain’s decentralisation, immutability, transparency and security to enhance carbon estimation accuracy and streamline carbon trading.
Seung Eel Oh, Jong‐Hoon Kim, Ji-Young Kim, Jae Hwan Ahn
The complexity of contemporary supply chains and the rise in foodborne illness cases have made ensuring food safety and traceability a top responsibility on a worldwide scale. Traditional traceability systems are prone to data tampering, fragmentation, and limited compatibility. Public blockchains have scalability, latency, and privacy problems that limit their use in real-time food safety systems, despite the fact that blockchain provides a secure data structure. Using Hyperledger Fabric, GS1 EPCIS standards, and Internet of Things-enabled environmental sensors, this paper suggests a private blockchain-based food safety monitoring system. To guarantee fault-tolerant, high-throughput processing in a permissioned blockchain setting, a Raft consensus mechanism was used. Hyperledger Caliper was used to benchmark the system once it was deployed with four nodes. According to experimental data, transaction throughput peaked at 230.2 TPS and averaged 207.4 ± 10.2 TPS. As the network grew from two to four nodes, latency increased somewhat from 259.3 ± 9.5 ms to 278.7 ± 9.1 ms, while block finalization time stayed below 3.184 ± 0.113 s. Over 114,925 documented transactions, data integrity was confirmed to be flawless. These results demonstrate that private blockchain technology can provide effective, scalable, and impenetrable food traceability, boosting openness and confidence throughout food networks.
This study explores the application of Quadratic Voting (QV) and its generalization to improve decentralization and effectiveness in blockchain governance systems. The conducted research identified three main types of quadratic (square root) voting. Two of them pertain to voting with a split stake, and one involves voting without splitting. In split stakes, Type 1 QV applies the square root to the total stake before distributing it among preferences, while Type 2 QV distributes the stake first and then applies the square root. In unsplit stakes (Type 3 QV), the square root of the total stake is allocated entirely to each preference. The presented formal proofs confirm that Types 2 and 3 QV, along with generalized models, enhance decentralization as measured by the Gini and Nakamoto coefficients. A pivotal discovery is the existence of a threshold stakeholder whose relative voting ratio increases under QV compared to linear voting, while smaller stakeholders also gain influence. The generalized QV model allows flexible adjustment of this threshold, enabling tailored decentralization levels. Maintaining fairness, QV ensures that stakeholders with higher stakes retain a proportionally greater voting ratio while redistributing influence to prevent excessive concentration. It is shown that to preserve fairness and robustness, QV must be implemented alongside privacy-preserving cryptographic voting protocols, as voters casting their ballots last could otherwise manipulate outcomes. The generalized QV model, proposed in this paper, enables algorithmic parametrization to achieve desired levels of decentralization for specific use cases. This flexibility makes it applicable across diverse domains, including user interaction with cryptocurrency platforms, facilitating community events and educational initiatives, and supporting charitable activities through decentralized decision-making.
Decentralised applications (dApps) that run on public blockchains have the benefit of trustworthiness and transparency as every activity that happens on the blockchain can be publicly traced through the transaction data. However, this introduces a potential privacy problem as this data can be tracked and analysed, which can reveal user-behaviour information. A user behaviour analysis pipeline was proposed to present how this type of information can be extracted and analysed to identify separate behavioural clusters that can describe how users behave in the game. The pipeline starts with the collection of transaction data, involving smart contracts, that is collected from a blockchain-based game called Planet IX. Both the raw transaction information and the transaction events are considered in the data collection. From this data, separate game actions can be formed and those are leveraged to present how and when the users conducted their in-game activities in the form of user flows. An extended version of these user flows also presents how the Non-Fungible Tokens (NFTs) are being leveraged in the user actions. The latter is given as input for a Graph Neural Network (GNN) model to provide graph embeddings for these flows which then can be leveraged by clustering algorithms to cluster user behaviours into separate behavioural clusters. We benchmark and compare well-known clustering algorithms as a part of the proposed method. The user behaviour clusters were analysed and visualised in a graph format. It was found that behavioural information can be extracted regarding the users that belong to these clusters. Such information can be exploited by malicious users to their advantage. To demonstrate this, a privacy threat model was also presented based on the results that correspond to multiple potentially affected areas.
With the widespread adoption of mobile smart devices, mobile crowd sensing(MCS) has provided better services for people. To meet the growing sensing demands within a limited budget, platforms have integrated two modes—opportunistic sensing and participatory sensing—to utilize their complementary strengths. However, location privacy issues may reduce workers' willingness to participate, thereby affecting task completion rates. Although existing methods have addressed privacy protection in a single sensing mode, there remains little focus on location privacy in hybrid sensing modes. There are two main limitations in privacy issues related to task allocation: (i) how to effectively preserve workers' location privacy in hybrid sensing modes, and (ii) the usual reliance on trusted thirdparty institution. To address these issues, we propose a privacypreserving hybrid multi-task allocation for MCS (PPHMA). This approach preserves workers' location privacy without relying on a fully trusted third-party institution, while maximizing the number of tasks completed. Specifically, for opportunistic task allocation, we employ zero-knowledge range proofs to protect workers' location , thereby avoiding location privacy leaks. Subsequently, based on the performance capability indicator of opportunistic workers, we select appropriate workers for task allocation. For participatory task allocation, we employ a worker location obfuscation generation algorithm to locally generate and upload obfuscated locations, ensuring that both the worker's real and obfuscated locations satisfy ϵ-Geo-Indistinguishability within the protected range. Then, based on the execution capability indicator of the participatory workers, we screen for candidate workers and use a greedy immune clone algorithm to optimize the workers' travel distances. Finally, we verify the effectiveness of the scheme through experiments using two real-world datasets
The proliferation of numerous portable mobile devices has made mobile crowd-sensing (MCS) systems a promising new trend. Traditional MCS systems typically outsource sensing tasks to the data aggregator (e.g., cloud server). They collect and analyze the provided sensing data through an appropriate truth discovery (TD) method to identify valuable data sets. However, existing privacy-preserving MCS systems lack transparency, enabling data aggregators to deviate from the specified protocols and allowing malicious users to provide false or invalid sensing data, thereby contaminating the resulting data sets. The lack of transparency and public verifiability in MCS systems undermines widespread adoption by preventing data requesters from confidently verifying data integrity and accuracy. To address this issue, we propose a transparent and privacy-preserving mobile crowd-sensing system with truth discovery (TP-MCS) constructed using zero-knowledge proof (ZKP) and the Merkle commitment tree. This scheme enables data requesters to effectively verify the correctness of the truth discovery service while ensuring data privacy. Furthermore, theoretical analysis and extensive experiments demonstrate that this scheme is secure and efficient.
Zheng Lu, Tao Feng, Zilong Xie, Xiaomin Li · 5 authors
Mobile crowdsensing (MCS) utilizes sensors distributed across different vehicles to support intelligent transportation and environmental monitoring. Recently, most of the research on MCS in Internet of Vehicles (IoV) mainly focuses on privacy protection and data security of anonymous authentication, but there are still shortcomings in reliability evaluation and reputation management of sensing vehicles. Besides, the lack of effective management of identity information may lead to difficulties in tracking malicious behavior. In this article, we propose a blockchain-assisted anonymous authentication and reputation management (BARM) scheme for MCS in IoV. Specifically, an efficient anonymous authentication algorithm is proposed for sensing vehicles. Then, the privacy protection reputation evaluation algorithm is proposed to ensure the reliability of the sensing vehicles and the security of sensing data. Meanwhile, an accurate reputation update algorithm is proposed to effectively check and update the reputation values of participating sensing vehicles. Besides, the smart contracts are written and deployed on the blockchain to manage the information of the sensing vehicles, which improves the security, efficiency, and trust of the identity management. Subsequently, a formal security verification method based on colored petri net (CPN) and Dolev-Yao attacker model is proposed to evaluate the security of the scheme. The evaluation results show that the scheme can effectively resist a variety of different types of attacks and has multiple security attributes. Performance analysis shows that the proposed scheme has low computation and communication overheads and high robustness.
While the trend of decentralized governance is obvious (cryptocurrencies and blockchains are widely adopted by multiple sovereign countries), initiating governance proposals within Decentralized Autonomous Organizations (DAOs) is still challenging, i.e., it requires providing a low-level transaction payload, therefore posing significant barriers to broad community participation. To address these challenges, we propose a multi-agent system powered by Large Language Models with a novel Label-Centric Retrieval algorithm to automate the translation from natural language inputs into executable proposal transactions. The system incorporates DAOLang, a Domain-Specific Language to simplify the specification of various governance proposals. The key optimization achieved by DAOLang is a semantic-aware abstraction of user input that reliably secures proposal generation with a low level of token demand. A preliminary evaluation on real-world applications reflects the potential of DAOLang in terms of generating complicated types of proposals with existing foundation models, e.g. GPT-4o.
This study focuses on automating the formation of ephemeral teams in the search for specialized professionals for software development projects characterized by well-defined requirements. In such cases, it is crucial to identify professionals with the specific skills necessary to meet the project’s overall needs. Two primary challenges emerge in defining an effective system. The first is the need to standardize the required skills. The second pertains to the application method that candidates should use to apply for specific positions, which should also facilitate automatic evaluation. In the proposed solution, we utilize a system of two smart contracts to ensure automation, security, and transparency in interactions. Additionally, the use of standard frameworks for skills, particularly the SFIA 9 framework, facilitates the precise mapping and classification of both required and possessed skills, aligning candidate selection with project requirements. This approach streamlines the recruitment and selection of ephemeral team members, reducing recruitment time and costs while enhancing the efficiency of software project delivery.
Many blockchain-based crowdsourcing frameworks currently struggle to address the high costs associated with on-chain storage and computation effectively, and they lack a quality-driven incentive mechanism tailored to bounding box annotation scenarios. To address these challenges, this paper proposes CrowdBA: A low-cost, quality-driven crowdsourcing architecture. The CrowdBA utilizes the Ethereum public blockchain as the foundational architecture and develops corresponding smart contracts. First, by integrating Ethereum with the InterPlanetary File System (IPFS), storage and computation processes are shifted off-chain, effectively addressing the high costs associated with data storage and computation on public blockchains. Additionally, the CrowdBA introduces a Dynamic Intersection over the union-weighted bounding box fusion (DWBF) algorithm, which assigns dynamic weights based on IoU to infer true bounding boxes, thereby assessing each worker’s annotation quality. Annotation quality then serves as a key criterion for incentive distribution, ensuring fair and appropriate compensation for all contributors. Experimental results demonstrate that the operational costs of each smart contract function remain within reasonable limits; the off-chain storage and computation approach significantly reduces storage and computation expenses, and the DWBF algorithm shows marked improvements in accuracy and robustness over other bounding box fusion methods.
Open access
Blockchain Technology Applications and Security
Mobile Crowdsensing and Crowdsourcing
Advanced Steganography and Watermarking Techniques
John Stephan, Matej Pavlovic, Antonio Locascio, Benjamin Livshits
Zero-Knowledge (ZK) rollups have become a popular solution for scaling blockchain systems, offering improved transaction throughput and reduced costs by aggregating Layer 2 transactions and submitting them as a single batch to a Layer 1 blockchain. However, the computational burden of generating validity proofs, a key feature of ZK rollups, presents significant challenges in terms of performance and decentralization. Current solutions rely on centralized infrastructure to handle the computational tasks, limiting the scalability and decentralization of rollup systems. This paper proposes CrowdProve, a prover orchestration layer for outsourcing computation to unreliable commodity hardware run by a broad community of small provers. We apply CrowdProve to proving transaction batches for a popular ZK rollup. Through our experimental evaluation, we demonstrate that community proving can achieve performance comparable to, and in some cases better than, existing centralized deployments. Our results show that even systems utilizing modest hardware configurations can match the performance of centralized solutions, making community-based proof generation a viable and cost-effective alternative. CrowdProve allows both the rollup operator and community participants to benefit: the operator reduces infrastructure costs by leveraging idle community hardware, while community provers are compensated for their contributions.
A user-friendly program called “Mini Wallet for Sending Ethers” was created to make utilizing private keys to send ethers (ETH) between wallets easier. This application, which prioritizes user-friendliness, enables users to connect to their Ethereum wallets—including well-known choices like MetaMask— via the Infura API. It enables customers to effectively manage their Ethereum transactions by reducing crypto currency transactions to a few clicks, doing away with a requirement to browse complicated wallet interfaces. Notably, the program guarantees the security and integrity of transactions by adhering to accepted block chain transaction protocols. The pertinent information is safely entered into a database following every transaction, creating a complete record of all transactions. Furthermore, the sender has easy access to details about the transaction, which improves Ethereum transactions’ accountability and transparency. For Ethereum aficionados looking for a hassle-free method to handle their crypto currency transactions, this cutting-edge technology offers an effective and user-friendly option.