In crowdsourcing systems, requesters publish tasks, and interested workers provide answers to get rewards. Worker anonymity motivates participation since it protects their privacy. Anonymity with unlinkability is an enhanced version of anonymity because it makes it impossible to ``link'' workers across the tasks they participate in. Another core feature of crowdsourcing systems is worker quality which expresses a worker's trustworthiness and quantifies their historical performance. Notably, worker quality depends on the participation history, revealing information about it, while unlinkability aims to disassociate the workers' identities from their past activity. In this work, we present AVeCQ, the first crowdsourcing system that reconciles these properties, achieving enhanced anonymity and verifiable worker quality updates. AVeCQ relies on a suite of cryptographic tools, such as zero-knowledge proofs, to (i) guarantee workers' privacy, (ii) prove the correctness of worker quality scores and task answers, and (iii) commensurate payments. AVeCQ is developed modularly, where the requesters and workers communicate over a platform that supports pseudonymity, information logging, and payments. In order to compare AVeCQ with the state-of-the-art, we prototype it over Ethereum. AVeCQ outperforms the state-of-the-art in three popular crowdsourcing tasks (image annotation, average review, and Gallup polls). For instance, for an Average Review task with $5$ choices and $128$ participating workers AVeCQ is 40\% faster (including overhead to compute and verify the necessary proofs and blockchain transaction processing time) with the task's requester consuming 87\% fewer gas units.
Blockchain can ensure data security and reliability during the stage of building operation and maintenance (BOM), provide reliable data for decision-making. However, existing schemes based on single-chain architecture have the problems of storage limitation and scalability, and ignore the impact of event's priority and real-time on blockchain transaction. Therefore, for BOM, this paper provides a BOM framework based on sharding blockchain (SBC-BOMF), which constructs two-layer architecture based on master-chain and multiple shards, relieves the storage pressure of blockchain nodes and improves the concurrency capability. Priority-based transaction handling strategy is designed to achieve reasonable and rapid response for multi-level transactions. Finally, an actual BOM project is taken as example to illustrate the effectiveness of proposed scheme; experiments are conducted for performance testing and evaluation. Results show that proposed scheme can effectively solve the scalability problem caused by the application of blockchain in BOM, reduce storage overhead, and realize efficient handling for blockchain transactions.
As the number of smart cars grows rapidly, vehicular crowdsensing (VCS) is gradually becoming popular. In a VCS infrastructure, sensing devices and computing units hold on smart cars as well as cloud servers form an IoT-edge-cloud continuum to perform real-time sensing tasks. In order to encourage the smart cars to participate in the real-time VCS process, blockchain technology can be combined with VCS to provide an automated incentive for VCS data trading without relying on trusted third parties. However, directly using blockchain to enforce the VCS data trading process incurs expensive service fees and participants still can conduct various misbehavior. In this article, we propose a secure blockchain-based data trading system for VCS named BTT system to address the above issues. In particular, we first integrate the blockchain-based data trading process with a lightweight privacy-preserving truth discovery algorithm to ensure the accuracy of sensing data while preserving data privacy. We then propose a gas-aware optimization mechanism to minimize the gas consumption of the data trading process. Finally, we carefully design a distributed judgment mechanism to regulate all participants to behave correctly in the data trading process. To demonstrate the practicability of our design, we implement a prototype of the BTT system deployed on an Ethereum test network and conduct extensive simulations.
As a result of the rapid development of Internet of Things (IoT) systems, an increasing number of academics are focusing on finding new applications for IoT systems. For IoT systems, crowdsourcing is a prevalent practise. Due to the large number of deployed devices in IoT networks, more research is still required on the privacy and trust issues that arise when utilizing crowdsourcing. As a result of the characteristics of social computing, the crowdsourcing network poses issues in terms of confidentiality and reliability. To consolidate and create this industry, we have built a differentially private crowdsourcing system that integrates public and private blockchains to address the privacy and trust issues of conventional crowdsourcing systems. Our proposed solution enables varying levels of privacy protection to protect the user’s identity and location. Moreover, the installation of blockchain networks might potentially ensure the data’s integrity. In the conclusion of this article, the possibility of deploying a crowdsourcing system with blockchain in IoE networks is examined.
Hussain Ahmad Madni, Rao Muhammad Umer, Gian Luca Foresti
Federated Learning (FL) is a machine learning technique, where collaborative and distributed learning is performed, while the private data reside locally on the client. Rather than the data, only gradients are shared among all collaborative nodes with the help of a central server. To ensure the data privacy, the gradients are prone to the deformation, or the representation is perturbed before sharing, ultimately reducing the performance of the model. Recent studies show that the original data can still be recovered using latent space (i.e., gradient leakage problem) by Generative Adversarial Network and different optimization algorithms such as Bayesian and Covariance Matrix Adaptation Evolution Strategy. To address the issues of data privacy and gradient leakage, in this paper, we train deep neural networks by exploiting the blockchain-based Swarm Learning (SL) framework. In the SL scheme, instead of sharing perturbed or noisy gradients to the central server, we share the gradients among authenticated (i.e., blockchain-based smart contract) training nodes. To demonstrate the effectiveness of the SL approach, we evaluate the proposed approach using the standard CIFAR10 and MNIST benchmark datasets and compare it with the other existing methods.
Crowdsourcing task success depends on the contributions of developers. How to identify capable developers and motivate them to actively contribute to a task is a challenging issue. This study investigates how the use of cryptocurrency rewards, i.e., the choices of stablecoins and unstablecoins affects the crowdsourcing task success, and how the relationship depends on task difficulty. Based on 3858 crowdsourcing tasks, we find that the use of unstablecoins reduces the number of participating contributors and extends the time period of having the first contributor, but has no significant effect on the likelihood of task success. In addition, task difficulty alleviates the negative effect of the unstablecoins on the number of participants. Our study potentially provides important implications for the use of cryptocurrency tokens as task rewards.
We introduce DARSAN, a decentralized review system designed for Non-Fungible Token (NFT) marketplaces, to address the challenge of verifying the quality of highly resalable products with few verified buyers by incentivizing unbiased reviews. DARSAN works by iteratively selecting a group of reviewers (called ``experts'') who are likely to both accurately predict the objective popularity and assess some subjective quality of the assets uniquely associated with NFTs. The system consists of a two-phased review process: a ``pre-listing'' phase where only experts can review the product, and a ``pre-sale'' phase where any reviewer on the system can review the product. Upon completion of the sale, DARSAN distributes incentives to the participants and selects the next generation of experts based on the performance of both experts and non-expert reviewers. We evaluate DARSAN through simulation and show that, once bootstrapped with an initial set of appropriately chosen experts, DARSAN favors honest reviewers and improves the quality of the expert pool over time without any external intervention even in the presence of potentially malicious participants.
In developing countries, funding is a significant obstacle to receiving higher education. Brilliant but needy students cannot complete their studies since their parents are unemployed and their countries’ economies are poor. As a result, the students’ talents are not harnessed to their full potential. Blockchain technology is a growing platform for making and storing secure and trustworthy transactions on distributed ledgers. In this work, we employ blockchain technology coupled with crowdsourcing to provide a loan platform to offer financial assistance to students in tertiary institutions in developing countries. This platform will allow students to apply for loans through registered financial institutions from registered investors and pay them back with interest when they start working. We perform a thorough security analysis and back the efficiency of our work with numerical results.
Open-source systems help to manage the rapid development of software, while governing open-source systems properly can effectively promote software and software engineering. However, some significant problems, such as code controls, incentives, interaction and cooperation, automation, transparency and fairness of rights and responsibilities, cannot be properly solved by traditional methodologies. The decentralization, immutability, change in trust mode and smart contract programming of blockchain provide new solutions. In order to solve the problems of traditional centralized open-source governance, this paper proposes a decentralized open-source coordination management system using a novel triple-blockchain architecture. Through the analysis of traditional and blockchain-based research, the business and technical issues that need to be addressed in decentralized open-source governance systems have been emphatically studied. Combined with triple-blockchain architecture, smart contracts, oracles and continuous integration tools, we study the decentralization of open-source businesses and make them more trustworthy, automated and coordinated. An identity authentication mechanism is designed for permission control and inter-community collaboration. A decentralized open-source reputation is proposed for incentive and reference. We also improved the DPoS (Delegated Proof of Stake) consensus under triple-blockchain architecture to reduce repeated elections. By constructing the OSCMS prototype based on the proposed architecture model, many comparative experiments were conducted under different parameters and conditions and showed good feasibility, scalability, reliability and performance. The OSCMS not only solves the shortcomings of previous research but also provides a comprehensive and feasible reference for the decentralized practice of open-source governance.
The recent explosive development of new forms of the digital currency creates unprecedented opportunities while posing significant governance challenges.These challenges call into question whether bitcoin should adopt an institutionalized framework.It also raises concerns about whether bitcoin governance following the path of internet governance will be a viable solution to the challenges.This paper investigates the concept of the bitcoin ecosystem, the governance challenges that the ecosystem faces, stakeholder roles in governance, and whether having an institutionalized framework will effectively minimize the challenges that bitcoin network users face.
The term ‘Web3’ refers to the practices of participating in digital infrastructures through the ability to read, write and coordinate digital assets. Web3 is hailed as an alternative to the failings of big tech, offering a participatory mode of digital self-organizing and shared ownership of digital infrastructure through software-encoded governance rules and participatory practices. Yet, very few analytical frameworks have been presented in academic literature by which to approach Web3. This piece draws on the theoretical lens of infrastructure studies to offer an analytical framework to approach the emergent field of Web3 as an exploration in ‘how to infrastructure’ through prefigurative self-infrastructuring. Drawing on qualitative examples from digital ethnographic methods, I demonstrate how the origins of Web3 reveal the intentions of its creators as a political tool of prefiguration, yet its practices reveal the inherent tension of expressing these ideals in coherent technical and institutional infrastructure. Thus, I argue that one of the fundamental challenges Web3 is negotiating through technical and governance experiments is ‘how to self-infrastructure?’.
Mengge Li, Miao Ma, Liang Wang, Bo Yang · 6 authors
With the rapid development of smart cities, vehicles equipped with various sensors can effectively sense traffic, thus forming a crowdsensing paradigm for the intelligent transportation system (ITS). Although mobile crowdsensing in ITS has broad application advantages, it still faces many challenges, such as single point of failure, inefficient independent task allocation, and the inability to deal with safety emergency tasks in time. To handle the abovementioned issues, we establish a decentralized ITS architecture based on blockchain and propose the concurrent tasks assignment problem proved to be NP-hard and safety emergency tasks assignment problem. Then, we propose reinforcement learning-based concurrent tasks and the safety emergency tasks assignment method, which can maximize the utility of concurrent tasks based on satisfying the requirements of safety emergency tasks. Simulation results demonstrate the effectiveness of the proposed methods.
The computation need of neural networks has out-paced the capabilities of many individual users whose computers, mobile devices, and other devices are relatively limited in computation power. To solve this problem, currently users need to offload the model training task to the cloud that has many computing resources. On the other hand, many devices on the edge have idling CPU cycles not used. Inspired by the successes of crowdsourcing and decentralized computing platforms such as blockchain and Web3, we propose to outsource an individual's neural network training task to edge devices, such that individuals can train their own neural network models without relying on the centralized cloud. Specifically, we design a divide-and-conquer learning framework in the edge computing environment. A user can divide the training computation of its neural network into neuron-sized computation tasks and distribute them to devices in the edge based on their available resources. The results will be returned to the user and aggregated in an iterative process to obtain the final neural network model. To protect the privacy of the user's data and model, shuffling is done to both the data and the neural network model before the computation task is distributed to edge nodes. Security against misbehaving edge nodes can also be provisioned by redundancy in task assignment.
During the global pandemic, information workers were abruptly forced to engage in virtual work. This paper reports from an experiment seeking to formalize the formalization of small team coordination at London Blockchain Lab through the use of blockchain supported tokenization. The Web3 organizing vision promotes the technology as an enabler of new ways for individuals and organizations to engage in the transparent exchange of scarce digital rights. However, little attention has been paid to the use of blockchain technologies to coordinate distributed collaborative activities. This paper seeks to understand the viability of this vision amongst a community of expected early adopters through design experimentation resulting in interview data. The study points towards the significant gap between the Web3 vision and the problems of realizing this in practice. This highlights fundamental barriers to using blockchain for team collaboration while also pointing toward its potential. Even the most willing and able find it hard to turn code into law through tokenizing collaboration.
Web 3.0 is the next-generation Internet that enables participants to read, write, and own contents in a decentralized manner. It is mainly driven by blockchain, semantic communication, edge computing, and artificial intelligence, which can construct value networks to achieve participatory economics based on participatory decision making. Web 3.0 can capture the characteristics of blockchain, semantic extraction, and communication to achieve decentralized semantic sharing and transfer information precisely. However, current Web 3.0 solutions focus on the blockchain while overlooking other new technologies' roles in Web 3.0. To further unleash the advantages of semantic extraction and communication in Web 3.0, in this article, we propose a blockchain-based semantic exchange framework to realize fair and efficient interactions. In this framework, we first attempt to tokenize semantic information into Non-Fungible Token (NFT) for semantic exchange. Then we utilize a Stackelberg game to maximize buying and pricing strategies for semantic trading. We also leverage Zero-Knowledge Proof to share authentic semantic information without publishing it before receiving payments, which can achieve a fair and privacy-preserving trading compared with current NFT marketplaces. A case study about urban planning is given to show clearly the proposed mechanisms. Finally, several challenges and opportunities are identified.
With the rapid growth of connected and autonomous vehicles (CAVs), vehicular crowdsensing (VCS) has emerged as an effective way in a wide range of applications, especially in intelligent transportation systems. However, centralized VCS frameworks have confronted many problems, such as privacy, security, utility, and dependability. To remedy these challenges, blockchain technology can be applied in VCS systems for effectively forming decentralized autonomous vehicles organizations and operations. This article briefly introduces blockchain-based VCS solutions addressing the current problems and presents potential directions for future research.
Mohammad Salimibeni, Zohreh Hajiakhondi-Meybodi, Arash Mohammadi, Yingxu Wang
Recently, as a consequence of the coronavirus disease (COVID-19) pandemic, dependence on contact tracing (CT) models has significantly increased to prevent the spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. This article addresses this gap and proposes the trustworthy blockchain-enabled system for an indoor CT (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof-of-Work (dPoW) credit-based consensus algorithm coupled with randomized hash window (W-Hash) and dynamic Proof-of-Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node’s behavior based on its underlying credit-based mechanism. For achieving a high localization performance, we capitalize on the availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data-driven localization model based on bluetooth low-energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by the implementation of a highly accurate CT model while improving the users’ privacy and security.
Federated Learning (FL) is a distributed machine learning paradigm that trains models across multiple devices without exchanging users’ data, thereby providing stronger data privacy guarantees. However, some research reveals that FL may face security and privacy issues, such as single point of failure, model poisoning, and parameter privacy disclosure. Recently, the field of combining blockchain and FL has a trend to become a hot research topic. More and more researchers attempt to use blockchain to decentralize and secure FL frameworks. To further understand recent advances in blockchain-based FL (BFL) systems, this article aims to provide a systematic survey to deconstruct BFL systems. We propose a taxonomy of BFL systems following the lifecycle of FL tasks and divide them into three layers,$i.e.$, the blockchain layer, the training layer, and the aggregation layer. We review and summarize representative work in each layer. We also discuss several open challenges for designing more secure and efficient BFL systems.
Federated learning is a privacy-preserving machine learning technique that trains models across multiple devices holding local data samples without exchanging them. There are many challenging issues in federated learning, such as coordinating participants’ activities, arbitrating their benefits, and aggregating models. Most existing solutions employ a centralized approach, in which a trustworthy central authority is needed for coordination. Such an approach incurs many disadvantages, including vulnerability to attacks, lack of credibility, and difficulty in calculating rewards. Recently, blockchain was identified as a potential solution for addressing the abovementioned issues. Extensive research has been conducted, and many approaches, methods, and techniques have been proposed. There is a need for a systematic survey to examine how blockchain can empower federated learning. Although there are many surveys on federated learning, few of them cover blockchain as an enabling technology. This work comprehensively surveys challenges, solutions, and future directions for blockchain-empowered federated learning (BlockFed). First, we identify the critical issues in federated learning and explain why blockchain provides a potential approach to addressing these issues. Second, we categorize existing system models into three classes: decoupled, coupled, and overlapped, according to how the federated learning and blockchain functions are integrated. Then we compare the advantages and disadvantages of these three system models, regard the disadvantages as challenging issues in BlockFed, and investigate corresponding solutions. Finally, we identify and discuss the future directions, including open problems in BlockFed.
Javier Arroyo, David Davó, Elena Martínez-Vicente, Youssef Faqir-Rhazoui · 5 authors
Decentralized Autonomous Organizations (DAOs) are a new kind of organization that relies on blockchain software to govern their projects. Typically, DAO members may put forward and vote on proposals. For instance these proposals may consist on someone doing some tasks in exchange for a share of the DAO crypto-funds. In recent times, DAOs have gained a remarkable adoption, and yet they are still understudied by the academic literature. In this work, we present a visual analytics tool to study DAO activity focusing on their participation and temporal evolution. Our tool will hopefully help to stimulate research on this new kind of online community and collaborative software.
Federated learning (FL) is a promising way to allow multiple data owners (clients) to collaboratively train machine learning models without compromising data privacy. Yet, existing FL solutions usually rely on a centralized aggregator for model weight aggregation, while assuming clients are honest. Even if data privacy can still be preserved, the problem of single-point failure and data poisoning attack from malicious clients remains unresolved. To tackle this challenge, we propose to use distributed ledger technology (DLT) to achieve FLock, a secure and reliable decentralized Federated Learning system built on blockchain. To guarantee model quality, we design a novel peer-to-peer (P2P) review and reward/slash mechanism to detect and deter malicious clients, powered by on-chain smart contracts. The reward/slash mechanism, in addition, serves as incentives for participants to honestly upload and review model parameters in the FLock system. FLock thus improves the performance and the robustness of FL systems in a fully P2P manner.