Electronic voting, or e-voting, offers fundamental advantages over paper-based systems, including increased efficiency and reduced errors. The electronic voting system aims to boost user participation by enabling individuals to cast their votes from any location using any device with an internet connection. Blockchain, an emerging decentralized technology with robust cryptographic foundations, holds the potential to enhance various industries. Integrating blockchain technology into e-voting systems could address current concerns and challenges, providing a promising solution for improvement. This paper proposes a blockchain-based voting system designed to mitigate voting fraud, simplify the voting process, and ensure security and efficiency through face recognition. The lack of adequate transparency in many voting systems presents a significant challenge to building trust among voters, making it difficult for the government to secure their confidence. The failure of traditional and current digital voting systems lies in their susceptibility to exploitation. To address this, the paper suggests a framework employing effective hashing techniques to ensure data security. The concept of block creation and sealing is introduced, emphasizing the implementation of hashing algorithms. The proposed framework discusses the effectiveness of the polling process, detailing the implementation of an adjustable blockchain method involving utility establishment, contract formation, block creation and sealing, data accumulation, and results declaration. This approach ensures a dynamic and flexible application of blockchain technology. Key Words: Blockchain, E-Voting, Smart Contract, Face Regconition.
IoT systems are widely used in various applications, including healthcare, agriculture, manufacturing, and smart cities. However, these systems still have limitations, such as lack of security, high latency, energy inefficiency, the inefficiency of bandwidth utilization, and shortage of automaticity. The integration of edge computing and blockchain into IoT has been proposed to address these limitations. Yet, this integration is challenging and has not been deeply investigated. This paper aims to conduct a review of the integration of edge computing and blockchain into IoT systems. To the best of our knowledge, this is the first review paper that covers all aspects of system architectures and categories of blockchain-based edge deployment, complete security requirements, including confidentiality, integrity, authentication, authorization/access control, privacy, trust/confidence, transparency, availability, secure automaticity, and tolerance, and applications of blockchain-based edge potential usages with consideration of security requirements. Additionally, this review provides comprehensive discussions of challenges and insights into the future direction of blockchain-based edge IoT systems. The review aims to serve as an entry point for non-expert readers and researchers to various aspects of blockchain-based edge IoT systems.
Burhan Ul Islam Khan, Khang Wen Goh, Mohammad Shuaib Mir, Nur Fatin Liyana Mohd Rosely · 6 authors
As the Internet of Things (IoT) continues to revolutionize value-added services, its conventional architecture exhibits persistent scalability and security vulnerabilities, jeopardizing the trustworthiness of IoT-based services. These architectural limitations hinder the IoT’s Sensor-as-a-Service (SEaaS) model, which enables the commercial transmission of sensed data through cloud platforms. This study proposes an innovative computational framework that integrates decentralized blockchain technology into the IoT architectural design, specifically enhancing SEaaS efficiency. This research contributes to an optimized IoT architecture with decentralized blockchain operations and simplified public key encryption. Furthermore, this study introduces an advanced SEaaS model featuring innovative trading operations for sensed data among diverse stakeholders. At its core, this model presents a unique blockchain-based data-sharing mechanism that manages multiple aspects, from enrollment to validation. Evaluations conducted in a standard Python environment indicate that the proposed SEaaS model outperforms existing blockchain-based data-sharing models, demonstrating approximately 40% less energy consumption, 18% increased throughput, 16% reduced latency, and a 25% reduction in algorithm processing time. Ultimately, integrating a lightweight authentication mechanism using simplified public key cryptography within the blockchain establishes the model’s potential for efficient and secure data-sharing in IoT.
Purpose The purpose of this paper is to study the following two issues regarding blockchain crowdsourcing. First, to design smart contracts with lower consumption to meet the needs of blockchain crowdsourcing services and also need to design better interaction modes to further reduce the cost of blockchain crowdsourcing services. Second, to design an effective privacy protection mechanism to protect user privacy while still providing high-quality crowdsourcing services for location-sensitive multiskilled mobile space crowdsourcing scenarios and blockchain exposure issues. Design/methodology/approach This paper proposes a blockchain-based privacy-preserving crowdsourcing model for multiskill mobile spaces. The model in this paper uses the zero-knowledge proof method to make the requester believe that the user is within a certain location without the user providing specific location information, thereby protecting the user’s location information and other privacy. In addition, through off-chain calculation and on-chain verification methods, gas consumption is also optimized. Findings This study deployed the model on Ethereum for testing. This study found that the privacy protection is feasible and the gas optimization is obvious. Originality/value This study designed a mobile space crowdsourcing based on a zero-knowledge proof privacy protection mechanism and optimized gas consumption.
The office attendance system has shifted from using physical forms to digital inputs to minimize data errors and data loss when taking attendance. Unfortunately, digital systems generally still use traditional databases where the admin's role is crucial, and there is potential for fraud (e.g., admitting attendance of a non-attending person or manipulating a targeted person’s log due to personal grudges, competition, or other reasons) if the admin is dishonest. In this paper, we propose Absenin, a blockchain-based office attendance system, which replaces the role of traditional databases with blockchain and smart contracts to make it secure from malicious admins and fair for other participants. We create an Attendance Smart Contract that will run on the Ethereum blockchain. Admins and employees will interact with this smart contract to carry out attendance system operations. Absenin is also designed to have real-time attendance data, but the attendance machine does not need to be connected to the Internet, which is a unique feature of our system that no previous works have attempted. Despite using blockchain and smart contracts, our evaluation results show that Absenin is able to produce relatively small processing delays, and gas usage on the blockchain is still far below the gas limit of the Ethereum mainnet. Therefore, we can assure that the system is feasible and can be applied to organizations with a scale of thousands, tens, or hundreds of thousands of employees.
We study long-term equilibria that arise in the token monetary policy, or tokenomics, design of proof-of-stake (PoS) blockchain systems that engage utility maximizing users and validators. Validators are system maintainers who get rewarded with tokens for performing the work necessary for the system to function properly, while users compete and pay with such tokens for getting a desired portion of the system service. <br/><br/>We study how the system service provision and suitable rewards schemes together can lead to equilibria with the following desirable characteristics (1) viability: the system keeps parties engaged, (2) decentralization and skin-in-the-game: multiple sufficiently invested validators are participating, (3) stability: the price path of the underlying token used to transact with the system does not change widely over time, and (4) feasibility: the mechanism is easy to implement as a smart contract, e.g., it does not require a fiat reserve on-chain to perform token buybacks or to perform bookkeeping of exponentially growing token holdings.<br/><br/>Our analysis enables us to put forward a novel generic mechanism for blockchain monetary policy that we call quantitative rewarding (QR). We investigate how to implement QR in single-token and two-token proof of stake (PoS) blockchain systems. The latter are systems that utilize one token for the users to pay the transaction fees and a different token for the validators to participate in the PoS protocol and get rewarded. Our approach demonstrates a concrete advantage of the two-token setting in terms of the ability of the QR mechanism to be realized effectively and provide good equilibria. Our analysis also reveals an inherent limitation of the single token setting in terms of implementing an effective blockchain monetary policy - a distinction that is, to the best of our knowledge, highlighted for the first time.licy - a distinction that is, to the best of our knowledge, highlighted for the first time.
Supported by cloud computing,FederatedLearning (FL) has experienced rapid advancement, as a promising technique to motivate clients to collaboratively train models without sharing local data. To improve the security and fairness of FL implementation, numerousBlockchain-empoweredFederatedLearning (BFL) frameworks have emerged accordingly. Among them, consensus algorithms play a pivotal role in determining the scalability, security, and consistency of BFL systems. Existing consensus solutions to block producer selection and reward allocation either focus on well-resourced scenarios or accommodate BFL based on clients' contributions to model training. However, these approaches limit consensus efficiency and undermine reward fairness, due to involving intricate consensus processes, disregarding clients' contributions during blockchain consensus, and failing to address lazy client problems (malicious clients plagiarizing local model updates from others to reap rewards). Given the aforementioned challenges, we make the first attempt to design a joint solution for efficient consensus and fair reward allocation in heterogeneous BFL systems with lazy clients. Specifically, we introduce a generalizable BFL workflow that can address lazy client problems well. Based on it, the global contribution of BFL clients is decoupled into five dominant metrics, and the block producer selection problem is formulated as a reward-constraint contribution maximization problem. By addressing this problem, the optimal block producer that maximizes global contribution can be identified to orchestrate consensus processes, and rewards are distributed to clients in proportion to their respective global contributions. To achieve it, we develop aContext-awareProof-of-Contribution consensus algorithm named CPoC to reach consensus and incentive simultaneously, followed by theoretical analysis of lazy client problems and privacy issues. Empirical results on widely-used datasets demonstrate the effectiveness of our design in improving consensus efficiency and maximizing global contribution.
Federated learning enables the collaborative training of machine learning models across multiple organizations, eliminating the need for sharing sensitive data. Nevertheless, in practice, the data distributions among these organizations are often non-independent and identically distributed (non-IID), which poses significant challenges for traditional federated learning. To tackle this challenge, we present a hierarchical federated learning framework based on blockchain technology, which is designed to enhance the training of non-IID data., protect data privacy and security, and improve federated learning performance. The framework builds a global shared pool by constructing a blockchain system to reduce the non-IID degree of local data and improve model accuracy. In addition, we use smart contracts to distribute and collect models and design a main blockchain to store local models for federated aggregation, achieving decentralized federated learning. We train the MLP model on the MNIST dataset and the CNN model on the Fashion-MNIST and CIFAR-10 datasets to verify its feasibility and effectiveness. The experimental results show that the proposed strategy significantly improves the accuracy of decentralized federated learning on three tasks with non-IID data.
Haiqin Wu, Boris Düdder, Shunrong Jiang, Liangmin Wang
Privacy-aware task allocation/matching has been an active research focus in crowdsourcing. However, existing studies focus on an honest-but-curious assumption and a single-attribute matching model. There is a lack of adequate attention paid to scheme designs against malicious behaviors and supporting user-side personalized task matching over multiple attributes. A few recent works employ blockchain and cryptographic techniques to decentralize the matching procedure with verifiable and privacy-preserving on-chain executions. However, they still bear expensive on-chain overhead. In this paper, we propose VP$^{2}$-Match, a blockchain-assisted (publicly) verifiable privacy-aware crowdsourcing task matching scheme with personalization. VP$^{2}$-Match extends symmetric hidden vector encryption for user-side expressive matching without compromising their privacy. It avoids costly on-chain matching by letting the blockchain only store evidence/proofs for public verifiability of the matching correctness and for enforcing fair interactions against misbehaviors. Specifically, we construct extended attribute sets and solve matching verification by an algorithmic reduction into subset verification with an accumulator for proof generation. Formal security proof and extensive comparison experiments on Ethereum demonstrate the provable security and better performance of VP$^{2}$-Match, respectively.
Mobile crowdsensing (MCS) is an efficient approach for large-scale sensing data collection by leveraging the mobility and capability of mobile devices. To avoid the weaknesses of traditional centralized crowdsensing systems, blockchain has been introduced to secure the process of MCS. This paper studies a location-aware scenario, where privacy of users are protected in a blockchain- based MCS system, and formulates an optimization problem to maximize the coverage given a budget based on reverse auction. An incentive mechanism named MMCB is further proposed and implemented as smart contracts in blockchain to solve the problem. We demonstrate that the mechanism achieves a set of desirable properties, including computation efficiency, individual rationality, truthfulness, budget feasibility, approximation, and privacy preservation. To protect the identity privacy of workers and obtain anonymity, a linkable ring signature is employed in smart contracts. In addition, a Pedersen commitment is utilized for protecting workers’ bid profile and the submitted sensing data is encrypted and only accessible to the requester. We implement a prototype system based on the Hyperledger Fabric platform, and the evaluation results show that our privacy-preserving incentive mechanism architecture improves 36.2% coverage and reduces 53.1% payment with better security level compared to the state-of-the-art schemes.
In recent years, the Decentralized Finance (DeFi) market has witnessed numerous attacks on the price oracle, leading to substantial economic losses. Despite the advent of truth discovery methods opening up new avenues for oracle development, it falls short in addressing high-value attacks on price oracle tasks. Consequently, this paper introduces a dynamically adjusted truth discovery method safeguarding the truth of high-value price oracle tasks. In the truth aggregation stage, we enhance future considerations to improve the precision of aggregated truth. During the credibility update phase, credibility is dynamically assessed based on the task's value and the Cumulative Potential Economic Contribution (CPEC) of information sources. Experimental results demonstrate a significant reduction in data deviation by 65.8\% and potential economic loss by 66.5\%, compared to the baseline scheme, in the presence of high-value attacks.
Rafael Belchior, Limaris Torres, Jonas Pfannschmidt, André Vasconcelos · 5 authors
With the evolution of distributed ledger technology (DLT), several blockchains that provide enhanced privacy guarantees and features, including Corda, Hyperledger Fabric, and Canton, are being increasingly adopted. These distributed ledgers only provide partial consistency, meaning that participants can observe the same ledger differently, i.e., observe some transactions but not others, providing higher levels of privacy to the end-user. Choosing privacy instead of transparency leads to delicate trade-offs that are difficult to manage during runtime, hampering the development of applications that depend on reasoning about shared state, e.g., asset transfers across blockchains. We propose using the concept of blockchain view (view) – an abstraction of the state a participant can access at a certain point to address this problem. Views allow us to systematically reason about either state partitions within the same DLT or an integrated view spanning across several DLTs. We introduce BUNGEE (Blockchain UNifier view GEnErator), the first DLT view generator, to allow capturing snapshots, constructing views from these snapshots, and merging views according to a set of rules specified by the view stakeholders. Creating views and operating views allows new applications built on top of dependable blockchain interoperability, such as stakeholder-centric snapshots for audits, cross-chain analysis, blockchain migration, and combined on-chain-off-chain analytics.
The precise characterization and modeling of Cyber-Physical-Social Systems (CPSS) requires more comprehensive and accurate data, which imposes heightened demands on intelligent sensing capabilities. To address this issue, Crowdsensing Intelligence (CSI) has been proposed to collect data from CPSS by harnessing the collective intelligence of a diverse workforce. Our first and second Distributed/Decentralized Hybrid Workshop on Crowdsensing Intelligence (DHW-CSI) have focused on principles and high-level processes of organizing and operating CSI, as well as the participants, methods, and stages involved in CSI. This perspective reports the outcomes of the latest DHW-CSI, focusing on Autonomous Crowdsensing (ACS) enabled by foundation intelligence and its associated technologies such as decentralized autonomous organizations and operations, large language models, and human-oriented operating systems. Specifically, we explain what ACS is and explore its distinctive features in comparison to traditional crowdsensing. Moreover, we present the “6A-goal” of ACS and propose potential avenues for future research.
Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models. According to this novel framework, multiple participants train a global model collaboratively, coordinating with a central aggregator without sharing their local data. As FL gains popularity in diverse domains, security, and privacy concerns arise due to the distributed nature of this solution. Therefore, integrating this strategy with Blockchain technology has been consolidated as a preferred choice to ensure the privacy and security of participants. This paper explores the research efforts carried out by the scientific community to define privacy solutions in scenarios adopting Blockchain-Enabled FL. It comprehensively summarizes the background related to FL and Blockchain, evaluates existing architectures for their integration, and the primary attacks and possible countermeasures to guarantee privacy in this setting. Finally, it reviews the main application scenarios where Blockchain-Enabled FL approaches have been proficiently applied. This survey can help academia and industry practitioners understand which theories and techniques exist to improve the performance of FL through Blockchain to preserve privacy and which are the main challenges and future directions in this novel and still under-explored context. We believe this work provides a novel contribution respect to the previous surveys and is a valuable tool to explore the current landscape, understand perspectives, and pave the way for advancements or improvements in this amalgamation of Blockchain and Federated Learning.
The precise characterization and modeling of Cyber-Physical-Social Systems (CPSS) requires more comprehensive and accurate data, which imposes heightened demands on intelligent sensing capabilities. To address this issue, Crowdsensing Intelligence (CSI) has been proposed to collect data from CPSS by harnessing the collective intelligence of a diverse workforce. Our first and second Distributed/Decentralized Hybrid Workshop on Crowdsensing Intelligence (DHW-CSI) have focused on principles and high-level processes of organizing and operating CSI, as well as the participants, methods, and stages involved in CSI. This letter reports the outcomes of the latest DHW-CSI, focusing on Autonomous Crowdsensing (ACS) enabled by a range of technologies such as decentralized autonomous organizations and operations, large language models, and human-oriented operating systems. Specifically, we explain what ACS is and explore its distinctive features in comparison to traditional crowdsensing. Moreover, we present the ``6A-goal" of ACS and propose potential avenues for future research.
Mobile Crowdsensing (MCS) is a paradigm where a crowdsourcer recruits a set of workers through a campaign to collect data using sensors in their mobile device. This process greatly reduces the costs of data collection processes; however, most of the historically proposed systems are centralized. Since this makes the MCS platform a single point of failure, there is an increasing interest in decentralized blockchain-based solutions; regardless, most of the current proposals have a vertical focus and do not account for the heterogeneity of MCS. We propose a decentralized high-level architecture for MCS, based on Distributed Ledger Technology (DLT), that is adaptable to most MCS deployments. We then implement our architecture using the IOTA protocols and evaluate its performance over a real deployment in terms of scalability, showing its advantages over classic blockchains for MCS data.
Abstract The built environment fundamentally suffers from organisational fragmentation in various aspects, such as data flow, finance, and supply chains. Blockchain technology can be considered a transformative solution to the inherent fragmentation of this industry. This chapter first defines the basics of blockchain technology to show how a peer-to-peer network could enable a decentralised, traceable, and immutable information system across the life cycles of built assets. Then, an overview of blockchain literature within the context of a circular economy, with real-life examples and the current state of blockchain adoption in the circular built environment, is presented, and the role that this technology plays in addressing certain circular strategies is discussed. Afterward, implementation challenges and incentives are identified to set realistic expectations regarding the capabilities of blockchain technologies. Emerging concepts within blockchain technologies are then presented to give insights into prospects beyond current literature and use cases in the circular built environment. Finally, the future of blockchain technology in a circular built environment is discussed to present the applicability of blockchain and its possible integration with other emerging digitalisation tools, such as building information modelling (BIM) and material passports, in wider domains of circular, smart cities and communities.
The development of intelligent sensors has garnered widespread attention in autonomous driving. Although they have made significant progress, current vehicular sensing systems are still limited to basic perceptual intelligence and lack deeper cognitive capabilities for comprehensive scene understanding. The emerging LLMs (Large Language Models) and agent technologies provide a promising solution to address these issues. This letter proposes a novel SensingAgent framework for building next-generation vehicular sensing systems toward new AI (Autonomous Intelligence and Agentic Intelligence). It adopts a cloud-edge-end architecture, leveraging multi-agent collaboration to revolutionize the sensing paradigm. Additionally, we introduce DAO (Decentralized Autonomous Organization) into sensing systems and propose a new concept of SAO (Sensor Autonomous Organization). It utilizes smart contracts to ensure trustworthy operations across the sensing industry chain. This letter presents a report on the Distributed/Decentralized Hybrid Workshop on Foundation/Infrastructure Intelligence (DHW-FII), providing new insights into the future of intelligent sensing systems.
Trustless tracking of Resident Space Objects (RSOs) is crucial for Space Situational Awareness (SSA), especially during adverse situations. The importance of transparent SSA cannot be overstated, as it is vital for ensuring space safety and security. In an era where RSO location information can be easily manipulated, the risk of RSOs being used as weapons is a growing concern. The Tracking Data Message (TDM) is a standardized format for broadcasting RSO observations. However, the varying quality of observations from diverse sensors poses challenges to SSA reliability. While many countries operate space assets, relatively few have SSA capabilities, making it crucial to ensure the accuracy and reliability of the data. Current practices assume complete trust in the transmitting party, leaving SSA capabilities vulnerable to adversarial actions such as spoofing TDMs. This work introduces a trustless mechanism for TDM validation and verification using deep learning over blockchain. By leveraging the trustless nature of blockchain, our approach eliminates the need for a central authority, establishing consensus-based truth. We propose a state-of-the-art, transformer-based orbit propagator that outperforms traditional methods like SGP4, enabling cross-validation of multiple observations for a single RSO. This deep learning-based transformer model can be distributed over a blockchain, allowing interested parties to host a node that contains a part of the distributed deep learning model. Our system comprises decentralised observers and validators within a Proof of Stake (PoS) blockchain. Observers contribute TDM data along with a stake to ensure honesty, while validators run the propagation and validation algorithms. The system rewards observers for contributing verified TDMs and penalizes those submitting unverifiable data.