Crowdsourcing is a process wherein an individual or an organisation utilizes\nthe talent pool present over the Internet to accomplish their task. The\nexisting crowdsourcing platforms and their reputation computation are\ncentralised and hence prone to various attacks or malicious manipulation of the\ndata by the central entity. A few distributed crowdsourcing platforms have been\nproposed but they lack a robust reputation mechanism. So we propose a\ndecentralised crowdsourcing platform having an immutable reputation mechanism\nto tackle these problems. It is built on top of Ethereum network and does not\nrequire the user to trust a third party for a non malicious experience. It also\nutilizes IOTAs consensus mechanism which reduces the cost for task evaluation\nsignificantly.\n
Location-based services (LBS) bring convenience to people's lives but are also accompanied with privacy leakages. To protect the privacy of LBS users, many location privacy protection algorithms were proposed. However, these algorithms often have difficulty to maintain a balance between service quality and user privacy. In this paper, we first overview the shortcomings of the existing two privacy protection architectures and privacy protection technologies, then we propose a location privacy protection method based on blockchain. Our method satisfies the principle of k-anonymity privacy protection and does not need the help of trusted third-party anonymizing servers. The combination of multiple private blockchains can disperse the user's transaction records, which can provide users with stronger location privacy protection and will not reduce the quality of service. We also propose a reward mechanism to encourage user participation. Finally, we implement our approach in the Remix blockchain to show the efficiency, which further indicates the potential application prospect for the distributed network environment.
Ouns Bouachir, Moayad Aloqaily, Lewis Tseng, Azzedine Boukerche
Blockchain has revolutionized how transactions are conducted by ensuring secure and auditable peer-to-peer coordination. This is due to both the development of decentralization, and the promotion of trust among peers. Blockchain and fog computing are currently being evaluated as potential support for software and a wide spectrum of applications, ranging from banking practices and digital transactions to cyber-physical systems. These systems are designed to work in highly complex, sometimes even adversarial, environments, and to synchronize heterogeneous machines and manufacturing facilities in cyber computational space, and address critical challenges such as computational complexity, security, trust, and data management. Coupling blockchain with fog computing technologies has the potential to identify and overcome these issues. Thus, this paper presents the knowledge of blockchain and fog computing required to improve cyber-physical systems in terms of quality-of-service, data storage, computing and security.
As a representative example for the construction of a smart city, a smart parking system has been developed in past research and implemented through IoT and cloud technologies. However, the initial installation cost of IoT sensor devices is preventing the spread of this technology, and thus as an alternative, a crowdsensing-based system, operating through data from publicly owned mobile devices, has been proposed. In this paper, we propose a multi-blockchain structure (i.e., constructed with public chain and private chain) in a crowdsensing-based smart parking system. In this structure, many sensing data contributors participate through the opened public blockchain, to transparently provide sensing information and to claim corresponding rewards. The private blockchain provides an environment for sharing the collected information among service providers in real time and for providing parking information to users. The bridge node performs an information relay role between the two blockchains. Performance analysis and security analysis on the implemented proposed system show the feasibility of our proposed system.
Over the years, the flourish of crowd computing has enabled enterprises to accomplish computing tasks through crowdsourcing in a large-scale and high-quality manner, and therefore how to efficiently and securely implement crowd computing becomes a hotspot. Some recent work innovatively adopted a P2P (peer-to-peer) network as the communication environment of crowdsourcing. Based on its decentralized control, issues like single-point-of-failure or DDoS attack can be overcome to some extent, but the huge computing capacity and storage costs required by this scheme is always unbearable. Federated learning is a distributed machine learning that supports local storage of data, and clients implement training through interactive gradient values. In our work, we combine blockchain with federated learning and propose a crowdsourcing framework named CrowdSFL, that users can implement crowdsourcing with less overhead and higher security. In addition, to protect the privacy of participants, we design a new re-encryption algorithm based on Elgamal to ensure that interactive values and other information will not be exposed to other participants outside the workflow. Finally, we have proved through experiments that our framework is superior to some similar work in accuracy, efficiency, and overhead.
Jannik Lockl, Vincent Schlatt, André Schweizer, Nils Urbach · 5 authors
The Internet of Things (IoT) describes the concept of physical objects equipped with identifying, sensing, networking, and processing capabilities being connected to the Internet. Architectures for the IoT typically rely on transmitting data to centralized cloud servers for processing. Although cloud services are supposed to enhance the IoT in storage, computation, and communication capabilities, this approach often generates isolated data silos and requires trust in third parties operating the cloud servers, which become single point of failure. In addition, centralized cloud-based applications lack transparency and allow for undetected manipulation and concealment of IoT data. To overcome these downsides, we develop and evaluate a blockchain-based IoT sensor data logging and monitoring system, employing a design science research approach. In this article, we show that such systems should provide modularity, data parsimony, and availability in addition to domain-specific principles. The prototype improves data integrity and availability but uncovers challenges, such as high operating costs through smart contract computation fees. Furthermore, semistructured interviews with practitioners allowed us to derive insights for developing blockchain-based IoT ecosystems and reveal that cooperation with organizations is key for transferring solutions into production. We contribute to the IoT knowledge base by providing design principles as well as managerial and technological recommendations.
Crowdworking platforms provide the opportunity for diverse workers to execute tasks for different requesters. The popularity of the ”gig” economy has given rise to independent platforms that provide competing and complementary services. Workers as well as requesters with specific tasks may need to work for or avail from the services of multiple platforms resulting in the rise of multi-platform crowdworking systems. Recently, there has been increasing interest by governmental, legal and social institutions to enforce regulations, such as minimal and maximal work hours, on crowdworking platforms. Platforms within multi-platform crowdworking systems, therefore, need to collaborate to enforce cross-platform regulations. While collaborating to enforce global regulations requires the transparent sharing of information about tasks and their participants, the privacy of all participants needs to be preserved. In this paper, we propose an overall vision exploring the regulation, privacy, and architecture dimensions for the future of work multi-platform crowdworking environments. We then present Separ, a multi-platform crowdworking system that enforces a large sub-space of practical global regulations on a set of distributed independent platforms in a privacy-preserving manner. Separ, enforces privacy using lightweight and anonymous tokens, while transparency is achieved using fault-tolerant blockchain ledgers shared among multiple platforms. The privacy guarantees of Separ against covert adversaries are formalized and thoroughly demonstrated, while the experiments reveal the efficiency of Separ in terms of performance and scalability.
Paolo Bellavista, Marco Cilloni, Giuseppe Modica, Rebecca Montanari · 6 authors
Nowadays, the exploitation of distributed ledger technology (DLT) is increasing among different domains and use cases. Not only within the context of cryptocurrencies, DLT could help the cooperation among untrusted parties in a wide variety of application scenarios. In particular, crowdsensing platforms can benefit from DLT because they need to federate systems belonging to different organizations to share end-user profiles, finally free to move within different domains, maintaining their identity. In this paper, we propose an edge-based distributed ledger architecture for supporting decentralised incentives in a specific mobile crowdsensing paltform called ParticipAct. To motivate the choice we describe two different deployments of ParticipAct, one based on a classical client-server architecture and the other one based on an edge-based model, and we highlight their pro and cons. In particular, our more notable findings rely on an approach based on edge computing and highlight how the three-tier solution improves the scalability, the performance, the security and the fault tolerance of the infrastructure responsible for the management of the federation among untrusted crowdsensing platforms.
Blockchain and other forms of Distributed Ledger Technology (DLT) provide an opportunity to integrate digital information, management, and contracts to increase trust and collaboration within the construction industry. DLT enables direct peer-to-peer transactions of value across a distributed network by providing an immutable and transparent record of these transactions. Furthermore, there is potential for business process optimization and automation on the transaction level through the use of smart contracts, which are code protocols deployed on supported DLT systems. However, DLT research in the construction industry remains at a theoretical level; there have been few implementation case studies to date. One potential reason for this is a knowledge gap between use-case ideas and the DLT technical system implementation. This paper aims to reduce this gap by (1) reviewing and categorizing proposed DLT use cases in construction literature, (2) providing an overview of DLT and its design options, (3) proposing an integrated framework to match DLT design options with desired characteristics of a use case, and (4) analysing the use cases using the new framework. Together, the use case categories and proposed decision framework can guide future implementers toward more connected and structured thinking between the technological properties of DLT and use cases in construction.
The world-changing blockchain technique provides a novel method to establish a secure, trusted and decentralized system for solving the security and personal privacy problems in Industrial Internet of Things (IIoT) applications. The mining process in blockchain requires miners to solve a proof-of-work puzzle, which requires high computational power. However, the lightweight IIoT devices cannot directly participate in the mining process due to the limitation of power and computational resources. The edge computing service makes it possible for IIoT applications to build a blockchain network, in which IIoT devices purchase computational resources from edge servers and thus can offload their computational tasks. The amount of computational resource purchased by IIoT devices depends on how many profits they can get in the mining process, and will directly affect the security of the blockchain network. In this paper, we investigate the incentive mechanism for the blockchain platform to attract IIoT devices to purchase more computational power from edge servers to participate in the mining process, thereby building a more secure blockchain network. We model the interaction between the blockchain platform and IIoT devices as a two-stage Stackelberg game, where the blockchain platform act as the leader, and IIoT devices act as followers. We analyze the existence and uniqueness of the Stackelberg equilibrium, and propose an efficient algorithm to compute the Stackelberg equilibrium point. Furthermore, we evaluate the performance of our algorithm through extensive simulations, and analyze the strategies of blockchain platform and IIoT devices under different situations.
With the popularization of intelligent terminals, especially current trends, such as “Industrie 4.0” and the Internet of Things, mobile crowdsensing is becoming one of the promising applications built on smart devices in mobile networks. However, the existing mobile crowdsensing models are mostly based on a centralized platform, which is not fully trusted in reality and results in the existence of fraud and other security problems. Furthermore, the data quality collected through crowdsensing is varied, and the location privacy is difficult to guarantee, especially at the worker selection stage. To solve these two problems, an effective blockchain-based location-privacy-preserving crowdsensing model, CrowdBLPS, is proposed in this article. First, the idea of a blockchain is introduced into this model. The decentralized structure and the consensus approach are applied to realize the nonrepudiation and nontampering of information. Second, to improve the data sensing quality and protect worker privacy, a two-stage approach, including the preregistration stage and the final selection stage, is proposed. Finally, we further implement a prototype on the Ethereum public testing network, and the experimental results show the feasibility, availability, and reliability of CrowdBLPS.
Youyang Qu, Longxiang Gao, Tom H. Luan, Yong Xiang · 7 authors
As the extension of cloud computing and a foundation of IoT, fog computing is experiencing fast prosperity because of its potential to mitigate some troublesome issues, such as network congestion, latency, and local autonomy. However, privacy issues and the subsequent inefficiency are dragging down the performances of fog computing. The majority of existing works hardly consider a reasonable balance between them while suffering from poisoning attacks. To address the aforementioned issues, we propose a novel blockchain-enabled federated learning (FL-Block) scheme to close the gap. FL-Block allows local learning updates of end devices exchanges with a blockchain-based global learning model, which is verified by miners. Built upon this, FL-Block enables the autonomous machine learning without any centralized authority to maintain the global model and coordinates by using a Proof-of-Work consensus mechanism of the blockchain. Furthermore, we analyze the latency performance of FL-Block and further derive the optimal block generation rate by taking communication, consensus delays, and computation cost into consideration. Extensive evaluation results show the superior performances of FL-Block from the aspects of privacy protection, efficiency, and resistance to the poisoning attack.
Personal IoT data is a new economic asset that individuals can trade to generate revenue on the emerging data marketplaces. Typically, marketplaces are centralized systems that raise concerns of privacy, single point of failure, little transparency and involve trusted intermediaries to be fair. Furthermore, the battery-operated IoT devices limit the amount of IoT data to be traded in real-time that affects buyer/seller satisfaction and hence, impacting the sustainability and usability of such a marketplace. This work proposes to utilize blockchain technology to realize a trusted and transparent decentralized marketplace for contract compliance for trading IoT data streams generated by battery-operated IoT devices in real-time. The contribution of this paper is two-fold: (1) we propose an autonomous blockchain-based marketplace equipped with essential functionalities such as agreement framework, pricing model and rating mechanism to create an effective marketplace framework without involving a mediator, (2) we propose a mechanism for selection and allocation of buyers' demands on seller's devices under quality and battery constraints. We present a proof-of-concept implementation in Ethereum to demonstrate the feasibility of the framework. We investigated the impact of buyer's demand on the battery drainage of the IoT devices under different scenarios through extensive simulations. Our results show that this approach is viable and benefits the seller and buyer for creating a sustainable marketplace model for trading IoT data in real-time from battery-powered IoT devices.
Yang Zhao, Jun Zhao, Jiawen Kang, Zehang Zhang · 7 authors
An increasing amount of users' sensitive information is now being collected for analytics purposes. To protect users' privacy, differential privacy has been widely studied in the literature. Specifically, a differentially private algorithm adds noise to the true answer of a query to generate a noisy response. As a result, the information about the dataset leaked by the noisy output is bounded by the privacy parameter. Oftentimes, a dataset needs to be used for answering multiple queries (e.g., for multiple analytics tasks), so the level of privacy protection may degrade as more queries are answered. Thus, it is crucial to keep track of the privacy spending which should not exceed the given privacy budget. Moreover, if a query has been answered before and is asked again on the same dataset, we may reuse the previous noisy response for the current query to save the privacy cost. In view of the above, we design and implement a blockchain-based system for tracking and saving differential-privacy cost. Blockchain provides a distributed immutable ledger that records each query's type, the noisy response used to answer each query, the associated noise level added to the true query result, and the remaining privacy budget in our system. Furthermore, since the blockchain records the noisy response used to answer each query, we also design an algorithm to reuse previous noisy response if the same query is asked repeatedly. Specifically, considering that different requests of the same query may have different privacy requirements, our algorithm (via a rigorous proof) is able to set the optimal reuse fraction of the old noisy response and add new noise (if necessary) to minimize the accumulated privacy cost. Experimental results show that the proposed algorithm can reduce the privacy cost significantly without compromising data accuracy.
Crowdsensing is an emerging paradigm of data aggregation, which has a pivotal role in data-driven applications. By leveraging the recruitment, a crowdsensing system collects a large amount of data from mobile devices at a low cost. The critical issues in the development of crowdsensing are platform security, privacy protection, and incentive. However, the existing centralized, platform-based approaches suffer from the single point of failure which may result in data leakage. Besides, few previous studies have addressed the considerations of both the economic incentive and data quality. In this paper, we propose a decentralized crowdsensing architecture based on blockchain technology which will help improve the attack resistance. Furthermore, we present a hybrid incentive mechanism, which integrates the data quality, reputation, and monetary factors to encourage participants to contribute their sensing data while discouraging malicious behaviors. The effectiveness our of proposed incentive model is verified through a combination of the theory of mechanism design. The performance analysis and simulation results illustrate that the proposed hybrid incentive model is a reliable and efficient mean to promote data security and incentivizing positive conduct on the crowdsensing application.
The aim of this paper is to understand whether Distributed Ledger\nTechnologies (DLTs) are ready to support complex services, such as those\nrelated to Intelligent Transportation Systems (ITS). In smart transportation\nservices, a huge amount of sensed data is generated by a multitude of vehicles.\nWhile DLTs provide very interesting features, such as immutability,\ntraceability and verifiability of data, some doubts on the scalability and\nresponsiveness of these technologies appear to be well-founded. We propose an\narchitecture for ITS that resorts to DLT features. Moreover, we provide\nexperimental results of a real test-bed over IOTA, a promising DLT for IoT.\nResults clearly show that, while the viability of the proposal cannot be\nrejected, further work is needed on the responsiveness of DLT infrastructures.\n
Deepak Puthal, Saraju P. Mohanty, Venkata P. Yanambaka, Elias Kougianos
In today's connected world, resource constrained devices are deployed for sensing and decision making applications, ranging from smart cities to environmental monitoring. Those recourse constrained devices are connected to create real-time distributed networks popularly known as the Internet of Things (IoT), fog computing and edge computing. The blockchain is gaining a lot of interest in these domains to secure the system by ignoring centralized dependencies, where proof-of-work (PoW) plays a vital role to make the whole security solution decentralized. Due to the resource limitations of the devices, PoW is not suitable for blockchain-based security solutions. This paper presents a novel consensus algorithm called Proof-of-Authentication (PoAh), which introduces a cryptographic authentication mechanism to replace PoW for resource constrained devices, and to make the blockchain application-specific. PoAh is thus suitable for private as well as permissioned blockchains. Further, PoAh not only secures the systems, but also maintains system sustainability and scalability. The proposed consensus algorithm is evaluated theoretically in simulation scenarios, and in real-time hardware testbeds to validate its performance. Finally, PoAh and its integration with the blockchain in the IoT and edge computing scenarios is discussed. The proposed PoAh, while running in limited computer resources (e.g. single-board computing devices like the Raspberry Pi) has a latency in the order of 3 secs.
Makoto Yano, Chris Dai, Kenichi Masuda, Yoshio Kishimoto
The Japanese Ministry of Economy, Trade, and Industry regards the process of incorporating new information technology, such as artificial intelligence (AI), Internet of Things (IoT), and big data analysis into society as the Fourth Industrial Revolution. This view is reflected in the Fifth Science and Technology Basic Plan. The plan advocates Society 5.0, in which cyber space and physical space are integrated to support an affluent and human-friendly society.
In recent years, cryptocurrencies implemented on top of Blockchains became very popular, with Bitcoin as the most prominent example. However, novel Blockchain-based platforms such as Ethereum also support distributed applications beyond cryptocurrencies through so-called smart contracts. Technically, smart contracts are programs, whose code and execution state is stored in the Blockchain, inherently featuring the ability to transfer (electronic) money during their execution. In this Bachelor thesis, we investigate how smart contracts can be used to implement a distributed crowdsensing application for tracking mobile objects by a crowd of privately owned mobile devices. Such a system could be used, for instance, to nd lost or stolen objects, such as keys, vehicles (cars, bicycles, . . . ), or pets tagged with short-range radio transmitters implemented using readily available Bluetooth or RFID technology. These objects can then be detected by smartphones of private users in the vicinity of the object, effectively implementing a huge sensor network covering many parts of the world without any upfront investments by a central entity. Although highly attractive, implementing a crowdsensing application on top of a Blockchain platform such as Ethereum comes with several challenges. First of all, users need incentives to participate in searching for mobile objects. A natural incentive is a monetary reward that participants automatically receive through the smart contract when reporting sightings (timestamped positions) of wanted objects. However, this directly brings up the problem of malicious participants (attackers) who try to get the reward without actually executing the work of searching for the object by simply reporting fake positions. Therefore, one major goal of this Bachelor thesis is to counter such attacks by proposing effective counter-measures, and implementing and evaluating them for the Ethereum platform. In detail, we propose a basic reputation-based approach for detecting fake positions which judges each sighting made by a mobile devices according to the reputation of that device, implemented by a smart contract. Furthermore, advanced attacks are identified compromising the basic reputation-based approach and effective counter-measures to these advanced attacks are proposed. Identified advanced attacks include reputation farming, where the attacker tries to aggregate reputation first before launching the attack, and the so-called copycat attack, where the attacker simply copies already submitted valid sightings form honest participants, making his fake positions indistinguishable from valid positions. Our evaluations analyses the monetary cost of executing smart contracts with and without our security mechanisms. The results show that the overhead included by our reputation-based approach is at maximum 45% of the cost of a smart contract without implemented security mechanisms.
Spatial crowdsourcing is an effective and novel method. In crowdsourcing systems, a centralized platform is traditionally used to allocate tasks and select workers. Centralized platforms always face following challenges: 1) How to ensure the rationality of tasks allocating; 2) How to ensure the payments of workers in the system when dishonest requesters exist; 3) How to ensure the maximum number of tasks are assigned. 4) How to ensure the integrity and reliability of the centralized platform. To solve these problems, this article proposed a distributed blockchain-based crowdsourcing framework - TSWCrowd (Task Select Worker Crowd). In this framework, tasks are sorted according to specific rules, thus tasks with higher priority are assigned to workers earlier. Workers who are available for a task will be selected and return a result. Then the deployed smart contracts will pay the basic payment automatically. At the same time, relevant contracts also calculate and pay the quality payment according to the proposed quality reward formulation. The proposed TSWCrowd framework on-chain involves a public dataset and uses solidity to compile the smart contracts. The framework was deployed on a local private blockchain. The decentralization property of the blockchain ensures the reliable assignment of tasks. Task-select-worker (TSW) algorithm sorts tasks to ensure reliability. In this paper, the proposed framework was compared with the ABCrowd auction mechanism on-chain and the VCG mechanism off-chain. The results show that the average distance is shorter and the payment is higher, thus reaches the reasonability, reliability and availability.
Naoki Kobayakawa, Mitsuyoshi Imamura, Kei Nakagawa, Kenichi Yoshida
Open source software (OSS) has become indispensable to our society. The success of OSS depends on the participation of a large number of developers or maintainers (contributors). Shedding light on the mechanisms of their participation has been an important academic and practical matter. One aspect to decide participation is the future prospects of a project. However, the causal mechanism behind participation has yet to be studied exhaustively and remains unclear. In this study, we used cryptocurrency projects, many of them were developed on GitHub, to better understand this mechanism. Both GitHub and cryptocurrencies are highly transparent, i.e., information is fully disclosed; we can analyze relevant information on a project, such as the contributors' activities, financial information, and development status. We adopted market capitalization as the substitution index of future prospects and the number of contributors and analyzed the relationship using time series analysis techniques, such as the Granger causality test and regression. We found that the number of contributors increases two months after market capitalization increases. This quantifies the impact of the future prospects of the project, i.e., of the market capitalization of a cryptocurrency, on the participation of contributors.
Jiawen Kang, Zehui Xiong, Chunxiao Jiang, Yi Liu · 9 authors
The emerging Federated Edge Learning (FEL) technique has drawn considerable attention, which not only ensures good machine learning performance but also solves "data island" problems caused by data privacy concerns. However, large-scale FEL still faces following crucial challenges: (i) there lacks a secure and communication-efficient model training scheme for FEL; (2) there is no scalable and flexible FEL framework for updating local models and global model sharing (trading) management. To bridge the gaps, we first propose a blockchain-empowered secure FEL system with a hierarchical blockchain framework consisting of a main chain and subchains. This framework can achieve scalable and flexible decentralized FEL by individually manage local model updates or model sharing records for performance isolation. A Proof-of-Verifying consensus scheme is then designed to remove low-quality model updates and manage qualified model updates in a decentralized and secure manner, thereby achieving secure FEL. To improve communication efficiency of the blockchain-empowered FEL, a gradient compression scheme is designed to generate sparse but important gradients to reduce communication overhead without compromising accuracy, and also further strengthen privacy preservation of training data. The security analysis and numerical results indicate that the proposed schemes can achieve secure, scalable, and communication-efficient decentralized FEL.
Crowdsourcing is a process wherein an individual or an organisation utilizes the talent pool present over the Internet to accomplish their task. The existing crowdsourcing platforms and their reputation computation are centralised and hence prone to various attacks or malicious manipulation of the data by the central entity. A few distributed crowdsourcing platforms have been proposed but they lack a robust reputation mechanism. So we propose a decentralised crowdsourcing platform having an immutable reputation mechanism to tackle these problems. It is built on top of Ethereum network and does not require the user to trust a third party for a non malicious experience. It also utilizes IOTAs consensus mechanism which reduces the cost for task evaluation significantly.
Federated learning (FL) is an emerging collaborative machine learning method to train models on distributed datasets with privacy concerns. To properly incentivize data owners to contribute their efforts, Shapley Value (SV) is often adopted to fairly assess their contribution. However, the calculation of SV is time-consuming and computationally costly. In this paper, we propose FedCoin, a blockchain-based peer-to-peer payment system for FL to enable a feasible SV based profit distribution. In FedCoin, blockchain consensus entities calculate SVs and a new block is created based on the proof of Shapley (PoSap) protocol. It is in contrast to the popular BitCoin network where consensus entities "mine" new blocks by solving meaningless puzzles. Based on the computed SVs, a scheme for dividing the incentive payoffs among FL clients with nonrepudiation and tamper-resistance properties is proposed. Experimental results based on real-world data show that FedCoin can promote high-quality data from FL clients through accurately computing SVs with an upper bound on the computational resources required for reaching consensus. It opens opportunities for non-data owners to play a role in FL.