Blockchain Papers

Ajit Muzumdar, Chirag Modi, C. Vyjayanthi

No abstract is available for this record.

Jiahao Qi, Feilong Lin, Zhongyu Chen, Changbing Tang · 6 authors

Federated learning is an emerging paradigm to conduct the machine learning collaboratively but avoid the leakage of original data. Then, how to motivate the data owners to participate federated learning and contribute high-quality data is the crucial issue. In this article, a blockchain-based federated learning (BFL) with a reputation mechanism for high-quality model aggregation is proposed. Specifically, the blockchain transforms the federated learning into a decentralized and trustworthy manner. Over the blockchain, federated learning tasks, undertaken by smart contracts, can be conducted transparently and fairly. Besides, a reputation-constrained data contribution and reward allocation mechanism is designed to encourage data owners to participate in BFL and contribute high-quality data. The noncooperative game is adopted to analyze the behavior strategies of data owners. The existence of the unique equilibrium is proved and the equilibrium point indicates that the data owners can acquire highest reward with the contribution of the highest quality data. Thus, the model quality of BFL is guaranteed. Finally, simulations on the public data sets (MNIST and CIFAR10) demonstrate that BFL with a reputation mechanism can well promote the high-quality model aggregation of federated learning as well as can prevent malicious nodes from corrupting the training task.

Shasha Li, Xiaodong Bai, Songjie Wei

Internet backboned crowdsourcing utilizes network-wide resources to solve complicated and large-scale tasks, which are not accomplishable for independent individuals. Existing crowdsourcing platforms are mostly centralized solutions with reliability and trustworthiness fragile to single-point failures on the central servers. The innovation of distributed ledgers as blockchain inspires us to optimize the traditional crowdsourcing procedure with distributed sustainability. We propose a blockchain-based design of the distributed secure crowdsourcing scheme for task distribution and result verification without relying on any third trusted institution. A preference-based task distribution (PTD) mechanism is presented which guarantees the percentage of task distribution and the satisfaction of the chosen workers. Task works are continuously assessed for reputations based on their historical behaviors. Task completion correctness is verified by blockchain consensus in two different scenarios after workers submit their results with reputations. We implement a prototype system based on the Ethereum chain with PTD and solution verification components. With various tasks and scenarios evaluated in the system, the proposed distributed crowdsourcing framework shows system reliability, data security, and scenario feasibility.

Liangxin Liu, Yanfang Fu, Yifan Ma, Zhiqiang Du · 6 authors

Crowdsensing of Internet of Vehicles (IoV) can make full of use of the mobility of vehicles to provide data collection services to individuals in deed. In this paper, we elaborate on the framework, operation procedures of both centralized and distributed crowdsensing frameworks in IoV, and analyze the system complexity, secure communication, data storage and other problems facing the current architecture. In order to solve these problems, this paper proposed a crowdsensing framework of IoV based on blockchain and smart contract on the basis of introducing vehicular edge computing; Secondly, a multi-level smart contracts interaction system were built which designed a detailed multi-module smart contract; Finally, a specific scheme for performing sensing tasks is presented on this framework, showing the communication between different entities. The simulation test and performance evaluation certify the availability, security and convenience of the framework.

Vandana Cp, S. Kalaivanan, R Karthik, A. Sanjana

Due to the quick increase of IoT devices, they lack the authentication standards and administration needed to keep user data secure. Hackers could cause significant infrastructure harm by infiltrating a wide spectrum of IoT devices. Blockchain use in IoT technology guarantees trust and authentication across all IoT elements, resulting in IoT security. Blockchain is a decentralized, distributed, and shared database that enables the creation of decentralized apps. Traceability, openness, immutability, and fault tolerance are some of the qualities of this technology that help maintain data privacy in IoT scenarios and thus create a safe environment. We look at a potential strategy for securely controlling IoT devices,i.e., devices connected to the internet using smart contracts on the blockchain in this study. This paper demonstrates how the proposed system comprising of a blockchain and smart contracts work efficiently in concurrence to avoid tampering by unauthorized parties. We have employed web3 library to control the linked devices by implementing Ethereum nodes (second most popular blockchain) on Raspberry Pi simulations and node.js.

Liang Gao, Li Li, Yingwen Chen, Chengzhong Xu · 5 authors

No abstract is available for this record.

Wei Tong, Xuewen Dong, Yulong Shen, Yuanyu Zhang · 6 authors

No abstract is available for this record.

Rafael Belchior, Luke Riley, Thomas Hardjono, André Vasconcelos · 5 authors

Entrepreneurs, enterprises, and governments are using distributed ledger technology (DLT) as a component of complex information systems, and therefore interoperability capabilities are required. Interoperating DLTs enables network effects, synergies and, similarly to the rise of the Internet, it unlocks the full potential of the technology. However, due to the novelty of the area, interoperability mechanisms (IM) are still not well understood, as interoperability is studied in silos. Consequently, choosing the proper IM for a use case is challenging. Our paper has three contributions: first, we systematically study the research area of DLT interoperability by dissecting and analyzing previous work. We study the logical separation of interoperability layers, how a DLT can connect to others (connection mode), the object of interoperation (interoperation mode), and propose a new categorization for IMs. Second, we propose the first interoperability assessment for DLTs that systematically evaluates the interoperability degree of an IM. This framework allows comparing the potentiality, compatibility, and performance among solutions. Finally, we propose two decision models to assist in choosing an IM, considering different requirements. The first decision model assists in choosing the infrastructure of an IM, while the second decision model assists in choosing its functionality.

Tao Peng, Kejian Guan, Jierong Liu

A mobile crowdsensing system (MCS) utilizes a crowd of users to collect large-scale data using their mobile devices efficiently. The collected data are usually linked with sensitive information, raising the concerns of user privacy leakage. To date, many approaches have been proposed to protect the users' privacy, with the majority relying on a centralized structure, which poses though attack and intrusion vulnerability. Some studies build a distributed platform exploiting a blockchain-type solution, which still requires a fully trusted third party (TTP) to manage a reliable reward distribution in the MCS. Spurred by the deficiencies of current methods, we propose a distributed user privacy protection structure that combines blockchain and a trusted execution environment (TEE). The proposed architecture successfully manages the users' privacy protection and an accurate reward distribution without requiring a TTP. This is because the encryption algorithms ensure data confidentiality and uncouple the correlation between the users' identity and the sensitive information in the collected data. Accordingly, the smart contract signature is used to manage the user deposit and verify the data. Extensive comparative experiments verify the efficiency and effectiveness of the proposed combined blockchain and TEE scheme.

Jian An, Zhenxing Wang, Xin He, Xiaolin Gui · 6 authors

With the rapid development of embedded smart devices, a new data collection paradigm, mobile crowd-sensing (MCS), has been proposed. MCS allows individuals from the crowd to act as sensors and contribute their observation data. However, existing MCS systems are mostly based on third-party platforms, and there is no guarantee that a center is completely credible. In addition, security and privacy issues should not be ignored. During MCS’ execution, the participants’ various information and truth value are usually exposed, and the computation related to data privacy cannot be verified. In this paper, we integrate the blockchain into the MCS scenario to design a blockchain based privacy-preserving quality control mechanism, which prevents data from being tampered with, and denied, ensuring that the reward is distributed fairly. In the new system, we propose a privacy preserving participant selection scheme and the result can be verified (i.e., security against malicious node) without any third-party arbiter. Finally, considering the issues with sensing data privacy and efficiency in the truth discovery process, we propose a new privacy-aware crowdsensing design with iterative truth discovery based on rational secure multi-party computation. The experimental results show that compared to the prior result, the proposed solutions are highly practical and facilitate quality control without violating the participant’s privacy.

Chi Zhang, Tao Shen, Fenhua Bai

With the rapid deployment of Internet of Things (IoT) devices in various industries and fields, the massive amount of data produced by these devices can yield greater value through sharing. A critical challenge in the data-sharing process is ensuring that the data are high quality. However, the quality of data provided by a large number of IoT devices is impacted by the variability of factors contributing to the data quality (DQ). Effective and safe sharing of perception data by the limited resources of IoT devices is a problem worth investigating. In this article, we propose a smart contract-based and DQ-driven incentive mechanism. First, a smart contract is proposed to realize security in the data-sharing process, while the proposed DQ evaluation mechanism ensures the quality of the shared data. Second, a two-layer Stackelberg game of nested coalitional (TLSNC) scheme is designed to obtain the maximum overall social welfare according to the trust score obtained during DQ evaluation while satisfying the limitation of loose and insufficient computing resources. Moreover, we designed a smart contract for automatic execution of the data-sharing transaction and used a trusted execution environment (TEE) to complete the security calculation of shared data. Finally, the numerical results reveal the effectiveness of the DQ evaluation mechanism and the security of our TEE-based model. Based on the proposed scheme, sustainable incentives for user participation and high-quality data sharing can be achieved. In addition, our system can significantly improve the overall social welfare compared to traditional solutions.

Kalkidan Gebru

The proliferation of IoT-based services for smart cities, and especially those related to mobility, are ever becoming more relevant and gaining attention from a number of stake-holders. In our work, we tackle the problem of characterizing people movements in a urban environment by using WiFi sensors connected to the cellular network. In particular, we leverage WiFi probe requests transmitted by people’s smartphones and a machine learning approach to detect people’s flows, while preserving users’ privacy. We validate our approach through a proof-of-concept testbed deployed in the proximity of our campus area. We consider two types of devices, namely, commercial, off-the-shelf WiFi scanners and ad-hoc designed scanners implemented with Raspberry PIs. They provide different levels of visibility of the captured traffic, preserving in different ways the privacy of the people’s movements. In our current work, we investigate the different trade-offs between mobility tracking accuracy and the level of provided people’s privacy.

Matevž Pustišek, Min Chen, Andrej Kos, Anton Kos

Blockchain ecosystems are rapidly maturing and meeting the needs of business environments (e.g., industry, manufacturing, and robotics). The decentralized approaches in industries enable novel business concepts, such as machine autonomy and servitization of manufacturing environments. Introducing the distributed ledger technology principles into the machine sharing and servitization economy faces several challenges, and the integration opens new interesting research questions. Our research focuses on data and event models and secure upgradeable smart contract platforms for machine servitization. Our research indicates that with the proposed approaches, we can efficiently separate on- and off-chain data and assure scalability of the DApp without compromising the trust. We demonstrate that the secure upgradeable smart contract platform, which was adapted for machine servitization, supports the business workflow and, at the same time, assures common identification and authorization of all the participants in the system, including people, devices, and legal entities. We present a hybrid decentralized application (DApp) for the servitization of 3D printing. The solution can be used for or easily adapted to other manufacturing domains. It comprises a modular, upgradeable smart contract platform and off-chain machine, customer and web management, and monitoring interfaces. We pay special attention to the data and event models during the design, which are fundamental for the hybrid data storage and DApp architecture and the responsiveness of off-chain interfaces. The smart contract platform uses a proxy contract to control the access of smart contracts and role-based access control in function calls for blockchain users. We deploy and evaluate the DApp in a consortium blockchain network for performance and privacy. All the actors in the solution, including the machines, are identified by their blockchain accounts and are compeers. Our solution thus facilitates integration with the traditional information-communication systems in terms of the hybrid architectures and security standards for smart contract design comparable to those in traditional software engineering.

Stefano Bistarelli, Marco Marcozzi, Gianmarco Mazzante, Leonardo Mostarda · 6 authors

No abstract is available for this record.

Maha Kadadha, Shakti Singh, Rabeb Mizouni, Hadi Otrok

Crowdsourcing is a rapidly growing paradigm that commercial platforms such as Amazon MTurk and UpWork are adopting for allocating tasks to workers. Such frameworks typically employ a centralized infrastructure to implement required mechanisms such as task allocation, submission evaluation, and payment computation. However, centralized deployment comes with unresolved challenges in terms of trust, reliability, and transparency. Blockchain technology has been embraced for the deployment of crowdsourcing frameworks to enable trusted and autonomous execution. Each of the existing Blockchain-based crowdsourcing/ crowdsensing framework targets a specific application context due to the constraint capabilities of Blockchain. In this paper, we propose a context-aware Blockchain-based crowdsourcing framework where the context is defined by task requirements and workers’ availability. The proposed framework is developed upon the review of existing works integrating Blockchain and crowdsourcing where the challenges and future directions are identified. The proposed framework has two classes of components: 1)core componentsimplementing the basic framework functionalities, and 2)advanced componentswhich are context and data managers that help improve the framework performance. TheAdvanced Context Manageris designed to monitor the current context and select the mechanisms to run for the core components accordingly. The core components are implemented as smart contracts on Blockchain for autonomous and trusted execution, while the advanced components are implemented spanning Blockchain and the cloud for flexibility and scalability. A case study demonstrating the performance of context-aware task allocation algorithms is presented. It shows how capturing the current system context can help achieve better overall performance based on the objective of the sensing application under consideration.

Zexin Wang, Biwei Yan, Anming Dong

In the machine learning, data sharing between different participants can increase the amount of data, improve the quality of the dataset, and thereby improve the quality of the model. Under the condition of data supervision, federated learning, as a distributed machine learning, aims to protect data while training models through collaboration among all parties to achieve data sharing and improve model quality. However, there are still some issues. For instance, the lack of trust between the participants makes it impossible to establish a secure and reliable sharing mechanism. In addition, how to fairly share the benefits generated by the model, identify honest participants and punish malicious participants is still a challenge. In this paper, we propose a new federated learning scheme based on blockchain architecture for federated learning data sharing. Moreover, an incentive mechanism based on reputation points and Shaply values is proposed to improve the sustainability of the federated learning system, which provides a credible participation mechanism for data sharing based on federated learning and fair incentives. The experimental results and analysis show that the loss of federated learning is more smooth than that of centralized machine learning.

Xinbin Xu, Jieren Cheng, Jingxin Liu, Yuming Yuan · 6 authors

No abstract is available for this record.

Junqin Huang, Linghe Kong, Long Cheng, Hong‐Ning Dai · 8 authors

Mobile crowdsensing (MCS) can promote data acquisition and sharing among mobile devices. Traditional MCS platforms are based on a triangular structure consisting of three roles: data requester, worker (i.e. , sensory data provider) and MCS platform. However, this centralized architecture suffers from poor reliability and difficulties in guaranteeing data quality and privacy, even provides unfair incentives for users. In this paper, we propose a blockchain-based MCS platform, namely BlockSense, to replace the traditional triangular architecture of MCS models by a decentralized paradigm. To achieve the goal of trustworthiness of BlockSense, we present a novel consensus protocol, namely Proof-of-Data (PoD), which leverages miners to conduct useful data quality validation work instead of “useless” hash calculation. Meanwhile, in order to preserve the privacy of the sensory data, we design a homomorphic data perturbation scheme, through which miners can verify data quality without knowing the contents of the data. We have implemented a prototype of BlockSense and conducted case studies on campus, collecting over 7,000 data from workers' mobile phones. Both simulations and real-world experiments show that BlockSense can not only improve system security, preserve data privacy and guarantee incentives fairness, but also achieve at least 5.6x faster than Ethereum smart contracts in verification efficiency.

Muhammad Shoaib Farooq, Zareen Kalim, Junaid Nasir Qureshi, Saim Rasheed · 5 authors

Distributed Agile Software Development (DASD) is the most important approach for the modern software industry that allows geographically independent software development. In the past, different tools and frameworks were proposed to solve communication and collaboration issues in DASD but they lacked transparency, trust, traceability, and security. These shortcomings resulted in project failure or overdue, customer dissatisfaction, project deal cancellations, and payment clashes between the customers and development teams. This paper addresses and overcomes the major issues of transparency, trust, security, traceability, coordination, and communication in DASD by embedding blockchain technology. We have proposed a novel blockchain-based framework named as AgilePlus which executes the smart contracts on a private ethereum blockchain for acceptance testing, secure payment, verification of developer’s payment requirements, and automatic payment distribution into the digital wallets of development teams. The execution of these smart contracts automatically assign penalties to the customers for late or non-payments and also to the developers for overdue tasks. Moreover, we have also solved the blockchain’s scalability challenge in AgilePlus by utilizing Interplanetary File System (IPFS) as off-chain storage. Lastly, experimental results prove that the proposed framework enhances transparency, communication, coordination, traceability, security and solves trust issues of both customers and developers in DASD.

Abdullah Yousafzai, Latif U. Khan, Umer Majeed, Owais Hakeem · 5 authors

Federated learning (FL) enables the training of a shared collaborative machine learning model while keeping all the confidential training data on distributed devices. The FL state-of-the-art considers a monopolist FL task publisher. However, we present a FL marketplace where multiple FL task publishers and mobile devices co-exist for a set of diverse and varying learning tasks. Mobile devices participating in the training of FL models provides pay-as-you-go (i.e. using blockchain-based cryptocurrencies) FL training services to the FL task publishers. In the proposed framework, multiple FL task publishers may compete with each other and the participating workers (i.e. mobile devices) can choose one FL task publisher over another for participation in the training of a global model. We utilize code offloading for enabling customized FL pipelines in mobile devices and mitigating the model heterogeneity inherent in varying and changing FL tasks published by the task publishers. Experimental results indicate the efficacy of the proposed framework.

Tassos Dimitriou, Antonis Michalas

In this work we develop a rewarding framework that can be used to enhance existing crowd-sensing applications. Although a core requirement of such systems is user engagement, people may be reluctant to participate as sensitive information about them may be leaked or inferred from submitted data. The use of monetary rewards can help incentivize participation, thereby increasing not only the amount but also the quality of sensed data. Our framework allows users to submit data and obtain Bitcoin payments in a privacy-preserving manner, preventing curious providers from linking the data or the payments back to the user. At the same time, it prevents malicious user behavior such as double-redeeming attempts, where a user tries to obtain rewards for multiple submissions of the same data. More importantly, it ensures thefairnessof the exchange in a completely trustless manner; by relying on the Blockchain, the trust placed on third parties in traditional fair exchange protocols is eliminated. Finally, our system is highly efficient as most of the protocol steps do not utilize the Blockchain network. When they do, only the simplest of Blockchain transactions are used as opposed to prior works that are based on the use of more complex smart contracts.

Taochun Wang, Huimin Shen, Jian Chen, Fulong Chen · 6 authors

With the continuous innovative development and popularization of mobile smart devices , the application of Mobile Crowd Sensing (MCS) continues to be studied extensively. However, existing centralized MCS applications that use servers for task publishing and data collection exhibit common problems, such as single points of failure and security vulnerabilities . Accordingly, we proposed a hybrid blockchain-based identity authentication scheme for MCS called HBIA, which uses blockchain technology to resolve the single-point failure problem. HBIA builds a cluster structure based on factors such as geographical location and balance, and uses it to construct a hybrid blockchain , with the cluster head node and internal cluster node authenticating on the public and private chains, respectively. We also implemented zero-knowledge proof (ZKP) to ensure the privacy of participants’ identities, thus balancing the contradiction between blockchain transparency and security. In addition, HBIA uses the zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) technology to enable off-chain computing and on-chain verification, further reducing the blockchain’s workload. Finally,​ HBIA was evaluated based on the pavement crack detection task and tested on the Ethereum public test network known as Ropsten. The test results indicate that the identity authentication scheme proposed in this paper is superior to existing schemes in terms of authentication time.

Paul van Vulpen, Jozef Siu, Slinger Jansen

Decentralized Autonomous Organizations (DAOs) have found use in the governance of Open Source Software (OSS) projects. However, the governance of an OSS producing DAO should match the particularities of OSS production while also overcoming the existing challenges of decentralized governance. Existing decentralized governance frameworks do not contain all the governance activities of open source software projects. Therefore, this study presents a governance framework for DAOs that produce open source software. The framework is built upon a total of 34 articles on DAO and OSS governance. The framework was evaluated in three leading DAOs that produce open source software. The evaluation underscored the significance of the framework and proved the potential of the systematic categorization of governance mechanisms. Finally, we list emerging governance practices in various governance domains in this developing field.

Mohammad Wazid, Ashok Kumar Das, Rasheed Hussain, Neeraj Kumar · 5 authors

No abstract is available for this record.