Due to the recent advancements in the Internet of Things (IoT) and Edge-Fog-Cloud Computing technologies, the Smart Public Safety (SPS) system has become a more realistic solution for seamless public safety services that are enabled by integrating machine learning (ML) into heterogeneous edge computing networks. While SPS facilitates convenient exchanges of surveillance data streams among device owners and third-party applications, the existing monolithic service-oriented architecture (SOA) is unable to provide scalable and extensible services in a large-scale heterogeneous network environment. Moreover, traditional security solutions rely on a centralized trusted third-party authority, which not only can be a performance bottleneck or the single point of failure, but it also incurs privacy concerns on improperly use of private information. Inspired by blockchain and microservices technologies, this paper proposed a BLockchain-ENabled Decentralized Smart Public Safety (BlendSPS) system. Leveraging the hybrid blockchain fabric, a microservices based security mechanism is implemented to enable decentralized security architecture, and it supports immutability, auditability, and traceability for secure data sharing and operations among participants of the SPS system. An extensive experimental study verified the feasibility of the proposed BlendSPS that possesses security and privacy proprieties with limited overhead on IoT based edge networks.
Advancements in distributed ledger technologies are rapidly driving the rise of decentralized crowdsourcing systems on top of open smart contract platforms like Ethereum. While decentralized blockchain-based crowdsourcing provides numerous benefits compared to centralized solutions, current implementations of decentralized crowdsourcing suffer from fundamental scalability limitations by requiring all participants to pay a small transaction fee every time they interact with the blockchain. This increases the cost of using decentralized crowdsourcing solutions, resulting in a total payment that could be even higher than the price charged by centralized crowdsourcing platforms. This paper proposes a novel suite of protocols called NF-Crowd that resolves the scalability issue by reducing the lower bound of the total cost of a decentralized crowdsourcing project to O(1). NF-Crowd is a highly reliable solution for scaling decentralized crowdsourcing. We prove that as long as participants of a project powered by NF-Crowd are rational, the O(1) lower bound of cost could be reached regardless of the scale of the crowd. We also demonstrate that as long as at least one participant of a project powered by NF-Crowd is honest, the project cannot be aborted and the results are guaranteed to be correct. We design NF-Crowd protocols for a representative type of project named crowdsourcing contest with open community review (CC-OCR). We implement the protocols over the Ethereum official test network. Our results demonstrate that NF-Crowd protocols can reduce the cost of running a CC-OCR project to less than $2 regardless of the scale of the crowd, providing a significant cost benefit in adopting decentralized crowdsourcing solutions.
Wei Feng, Zheng Yan, Laurence T. Yang, Qinghua Zheng
Mobile crowdsourcing (MCS) has become an effective data collection method due to its mobility, low cost, and flexibility. However, since centralized MCS confronts severe security and privacy risks in reality, many researchers are devoted to building a decentralized MCS system based on blockchain. Despite the effectiveness of these schemes, they fail to offer anonymous authentication on the trust of MCS nodes, although privacy is a main concern in MCS and trust plays an important role in a series of MCS activities, such as worker selection and truth discovery. Nevertheless, anonymous authentication on trust is not a trivial issue since trust evaluation usually conflicts with anonymity, which is a necessary privacy requirement in an open MCS environment. To tackle this problem, we leverage Intel software guard extension (SGX) and propose a scheme to anonymously authenticate trust with trustworthy trust evaluation in a blockchain-based MCS system. The scheme employs an SGX-enabled cloud server to periodically alter user public/private key pairs and mix newly altered keys among a number of faked keys in order to ensure unlinkability. Besides, we consider the unique features of MCS and work out a novel trust evaluation method by aggregating both subjective feedback and objective behaviors. Finally, we conduct several analyses and experiments to illustrate its security and efficiency.
Real-time traffic monitoring is a fundamental mission in a smart city to understand traffic conditions and avoid dangerous accidents. In this article, we propose a reliable and efficient traffic monitoring system that integrates blockchain and the Internet of Vehicles technologies effectively. It can crowdsource its tasks of traffic information collection to vehicles that run on the road instead of installing cameras in every corner. First, we design a lightweight blockchain-based information trading framework to model the interactions between traffic administration and vehicles. It guarantees reliability, efficiency, and security during executing trading. Second, we define the utility functions for the entities in this system and come up with a budgeted auction mechanism that motivates vehicles to undertake the collection tasks actively. In our algorithm, it not only ensures that the total payment to the selected vehicles does not exceed a given budget but also maintains the truthfulness of the auction process that prevents some vehicles from offering unreal bids for getting greater utilities. Finally, we conduct a group of numerical simulations to evaluate the reliability of our trading framework and performance of our algorithms, whose results demonstrate their correctness and efficiency perfectly.
Gerardo Canfora, Andrea Di Sorbo, Sonia Laudanna, Anna Vacca · 5 authors
Nowadays, more and more applications are developed for running on a distributed ledger technology, namely dApps. The business logic of dApps is usually implemented within smart contracts developed through Solidity, a programming language for writing smart contracts on different blockchain platforms, including the popular Ethereum. In Ethereum, the smart contracts run on the machines of miners and the gas corresponds to the execution fee compensating such computing resources. However, the deployment and execution costs of a smart contract depend on the implementation choices done by developers. Unappropriated design choices could lead to higher gas consumption than necessary. In this paper, we (i) identify a set of 19 Solidity code smells affecting the deployment and transaction costs of a smart contract, and (ii) assess the relevance of such smells through a survey involving 34 participants. On top of these smells, we propose GasMet, a suite of metrics for statically evaluating the code quality of a smart contract from the gas consumption perspective. An experiment involving 2,186 smart contracts demonstrates that the proposed metrics have direct associations with deployment costs. The metrics in our suite can be used for more easily identifying source code segments that need optimizations.
Andrea Di Sorbo, Sonia Laudanna, Anna Vacca, Corrado Aaron Visaggio · 5 authors
Nowadays, more and more applications are developed for running on a distributed ledger technology, namely dApps. The business logic of dApps is usually implemented within smart contracts developed through Solidity, a programming language for writing smart contracts on different blockchain platforms, including the popular Ethereum. In Ethereum, the smart contracts run on the machines of miners and the gas corresponds to the execution fee compensating such computing resources. However, the deployment and execution costs of a smart contract depend on the implementation choices done by developers. Unappropriated design choices could lead to higher gas consumption than necessary. In this paper, we (i) identify a set of 19 Solidity code smells affecting the deployment and transaction costs of a smart contract, and (ii) assess the relevance of such smells through a survey involving 34 participants. On top of these smells, we propose GasMet, a suite of metrics for statically evaluating the code quality of a smart contract from the gas consumption perspective. An experiment involving 2,186 smart contracts demonstrates that the proposed metrics have direct associations with deployment costs. The metrics in our suite can be used for more easily identifying source code segments that need optimizations.
With the increase in the number of on-board sensors, vehicles have shown great potential in mobile crowdsensing. To ensure the capability of the vehicular crowdsensing system, it is necessary to inspire sufficient vehicles to participate. However, due to personal interests and privacy protection, this goal is not easy to achieve. In addition, uncertain mobility of vehicles also brings challenges to the design of incentive mechanism. In this paper, we propose an incentive mechanism for nondeterministic vehicular crowdsensing with blockchain (INVCB), which can effectively incentivize vehicles while protecting user privacy. We first propose a framework for nondeterministic vehicular crowdsensing with blockchain and design a series of smart contracts to automate the crowdsensing process. Then, in order to improve the quality of sensing data, we add reputation attribute to each user, and provide an incentive mechanism that considers reputation. Extensive simulation results show the performance of our proposal is reliable and effective.
Junwei Zhang, Fan Yang, Zhuo Ma, Zhuzhu Wang · 6 authors
With the rapid development of Internet of Vehicles (IoV), vehicle-based spatial crowdsourcing (SC) applications have been proposed and widely applied to various fields. However, location privacy leakage is a serious issue in spatial crowdsourcing because workers who participate in a crowdsourcing task are required to upload their driving locations. In this paper, we propose a decentralized location privacy-preserving SC for IoV, which allows vehicle users to securely participate in SC with ensuring the task's location policy privacy and providing multi-level privacy preservation for workers' locations. Specifically, we introduce blockchain technology into SC, which can eliminate the control of vehicle user data by SC-server. We combine the additively homomorphic encryption and circle-based location verification to ensure the confidentiality of task's location policy. To achieve multi-level privacy preservation for workers' driving locations, we only reveal a grid where workers are located in. The size of the grid represents the level of privacy preservation. We leverage the order-preserving encryption and non-interactive zero-knowledge proof to prevent workers from illegally obtaining rewards by forging their driving locations. The security analysis results show that our framework can satisfy the above requirements. In addition, the experiment results demonstrate that our framework is efficient and feasible in practice.
Dingjie Sheng, Mingjun Xiao, An Liu, Xiang Zou · 6 authors
Crowdsourcing data trading is a novel paradigm in which the crowdsourcing technology is adopted to collect big data for trading. At present, existing crowdsourcing data trading systems usually depend on a trusted broker and haven't considered the truthfulness and the quality of data (QoD) simultaneously. Besides, copyright protection is the another issue that has not been properly addressed. To tackle these problems, we propose a Copyright-Preserving crowdsourcing data trading framework based on Blockchain, named CPchain, which mainly includes a smart contract. We design an auction algorithm based on semantic similarity to guarantee the truthfulness and individual rationality while ensuring QoD. Moreover, we combine digital fingerprint technology with blockchain to protect data copyright without a third-party certification authority. Furthermore, we develop a simple prototype of our proposed trading framework on the Ethereum test network. We have carried out a lot of experiments to demonstrate the significant performances of our framework.
Security and traceability of smart sensor data in centrally organized IoT-architectures require a third party of trust. In order to overcome this issue, Distributed Ledger Technologies (DLT) apply consensus mechanisms. Current approaches suggest DLT-based IoT-architectures which are static and only provide limited data precision in specific applications. Thus, they rely on custom tokens and additional technologies such as SQL databases. In addition, the design of the applied smart contracts (sc) allow unauthorized access. In contrast, in this paper an adaptable, scalable and purely DLT-based IoT-architecture for secure and decentral software services is proposed. It employs sc for the secure and decentralized interaction between users, software services and IoT devices, such as smart sensors. Thereby, sc are adjustable and their access is controlled by an address comparison of authorized wallets. Finally, a case-study on a sc based software service for an industrial smart temperature sensor demonstrates applicability and benefits of the proposed approach.
In this work we develop a rewarding framework that can be used as a building block in crowd-sensing applications. Our protocol 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, we thwart malicious user behavior such as double-redeeming attempts where a user tries to obtain rewards for multiple submissions of the same data. More importantly, we ensure the fairness of the exchange; by relying on the Blockchain, we eliminate the trust placed on third parties in traditional fair exchange protocols. Finally, our system is highly efficient as most of the protocol steps do not utilize the Blockchain network. When they do, we only rely on simple Bitcoin transactions as opposed to prior works that are based on the use of highly complex smart contracts.
Mohammad Hossein Chinaei, Hassan Habibi Gharakheili, Vijay Sivaraman
Verification of data generated by wearable sensors is increasingly becoming of concern to health service providers and insurance companies. These devices are typically vulnerable to a wide range of cybersecurity attacks, attempting to manipulate sensing data. Most of these disastrous attacks would remain undetected since neither healthcare servers nor Internet-of-Things (IoT) sensors are aware of the existence of attackers in the middle of communication. Thus, there is a need for a verification framework that various authorities can request a verification service for the local network data of a target IoT device. In this article, we leverage blockchain as a distributed platform to realize an on-demand verification scheme. This allows authorities to automatically transact with connected devices for witnessing services. A public request is made for witness statements on the data of a target IoT that is transmitted on its local network, and subsequently, devices (in close vicinity of the target IoT) offer witnessing service. Our contributions are threefold: 1) we develop a system architecture based on blockchain and smart contract that enables authorities to dynamically avail a verification service for data of a subject device from a distributed set of witnesses which are willing to provide (in a privacy-preserving manner) their local wireless measurement in exchange of monetary return; 2) we then develop a method to optimally select witnesses in such a way that the verification error is minimized subject to monetary cost constraints; and 3) finally, we evaluate the efficacy of our scheme using real Wi-Fi session traces collected from a five-storeyed building with more than thirty access points, representative of a hospital. According to the current pricing schedule of the Ethereum public blockchain, our scheme enables healthcare authorities to verify data transmitted from a typical wearable device with the verification error of the order 0.01% at cost of less than $ 2 for 1-hr witnessing service.
Software crowdsourcing is an emerging approach to software engineering with great potential for the subdivision and assignment of large-scale tasks. However, because of the centralization of the traditional crowdsourcing platform, information disclosure and nontransparent accounting may be difficult to avoid. To address this issue, we first introduce a novel blockchain-enabled crowdsourcing platform that integrates the functions of task assignment and resource lending via two dedicated smart contracts. Second, to ensure financial stability in the blockchain-enabled market and to match the difficulty of the received tasks with the ability of the workers, we design a dynamic, hierarchical pricing mechanism based on economic modeling methods and heterogeneous agent theory. With this mechanism, the market is divided dynamically into multiple levels according to the remuneration of the customers' offer and the market value of the workers' resources. Additional constraints are proposed to avoid possible malicious trading behavior from workers in the resource lending process. We prove theoretically the rationality of our model and demonstrate the dynamics of the model. We show that the market price and demand can be convergent and test the cost of executing the two smart contracts. Finally, extensive experimental results demonstrate the correctness and feasibility of the platform and confirm that the hierarchical pricing mechanism can maintain the stability of the market.
Ella Tallyn, Joe Revans, Evan Morgan, Dave Murray-Rust
This paper presents GeoPact, an assembly of technological objects that materialises location-aware smart contracts using internet of things and digital ledger technologies. Such contracts may facilitate the creation of distributed systems and services for transport and logistics that are locally constructed and adaptable, thus supporting specific community needs and sustainable objectives. However the technological infrastructures that underpin these systems are complex, making it difficult to engage publics in design processes. GeoPact grounds infrastructure in relatable physical activities, that are linked with holistic views of the system, and creates new experiences for public engagement. In these activities participants were invited to roleplay as couriers, and to progress through delivery scenarios which were governed by smart contracts. Participants and spectators were then encouraged to discuss their reactions, concerns and ideas. This paper illustrates the GeoPact assembly and reflects on our engagement activities.
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.
Smart vehicles can cooperate in teams to perform crowdsensing tasks in smart cities. A critical challenge in this regard is to build a secure model for nondeterministic vehicle teams to achieve maximum social welfare. Although several crowdsensing models have been proposed, none of them has focused on real-time vehicle teamwork. In this article, to the best of our knowledge, we propose the first secure model, called blockchain-based nondeterministic teamwork cooperation (BNTC), for nondeterministic teamwork cooperation in a vehicular crowdsensing system. We model the system as a multiconditional NP-complete problem by explicitly considering the dynamic features of task issuers and workers. To solve the problem, we propose the winning teams selected (WTS) algorithm based on a reverse auction and utilize a knapsack-based method to solve the models. We consider the credit of teams for determining the payment. Thus, we propose a credit-based team payment (CTP) algorithm for BNTC to maximize the welfare of the system. We also propose a general blockchain-based framework to address trust issues and security challenges to make the method suitable for use in practical applications. Based on theoretical analyses and extensive simulations, we demonstrate that the proposed model performs better than the baselines and can achieve the maximum social welfare. Implementation with Ethereum suggests our model can operate within a reasonable cost.
Mobile crowdsensing (MCS) is an emerging data acquisition technique that combines crowdsourcing with mobile devices to collect massive data in a cost-satisfactory manner. Two notable challenges of MCS are leakage of privacy and the challenge of malicious users, privacy-preserving reputation management scheme is an efficient method to tackle these challenges. However, most existing schemes rely on a semi-honest server and process data in plaintext domain without considering single point of failure and privacy of participants. In this paper we propose a reputation management scheme with blockchain to identify malicious users and protect users’ privacy simultaneously in MCS scenario. The secure and open nature of blockchain are exploited to build a dependable and efficient reputation management platform. Moreover, we adopt a distributed computing algorithm, Eigentrust, to construct a distributed reputation management framework, nevertheless, it neglects to preserve users’ privacy. So we leverage a verifiable secret sharing scheme into Eigentrust algorithm, which can prevent users’ personal information from being disclosed. The extensive analysis and experiments performed on EOS blockchain demonstrate that our system can effectively identify malicious users while preserving privacy.
Federated learning enables participants to collaborate on model training without directly exchanging raw data. Existing federated learning methods often follow the parameter server architecture, using third-party collaborators to provide aggregation and key management. In this case, the central node obtains information uploaded by other nodes. Studies have shown that with this information, the central node can infer important information, which leads to data privacy leakage. In addition, the failure on the server node can also cause the entire system to fail. We designed a completely decentralized federated learning framework based on blockchain, thereby avoiding the privacy and failure risk of the centralized structure. Moreover, we develop the corresponding model training approach. Compared with the existing methods, our framework performs better in terms of accuracy, robustness, and privacy.
Blockchain based applications benefit from decentralization, data privacy, and anonymity. However, they may suffer from inefficiency due to underlying blockchain. In this paper, we aim to address this limitation while still enjoying the privacy and anonymity. Taking the blockchain based crowdsourcing system as an example, we propose a new smart contract based cyber-insurance framework, which can greatly shorten the delay, and enable the workers to obtain the economic compensation for increased security risk caused by a conflict between the need to provide service quickly and delay in payment. We model the process of determining insurance premium and number of confirmations as a Stackelberg Game and prove the existence of Stackelberg Equilibria, at which the utility of the requester is maximized, and none of the workers can improve its utility by unilaterally deviating from its current strategy. The experimental results show that our framework can definitely improve the time efficiency of crowdsourcing. Particularly, it takes on average only 33% of the time required by the naive blockchain based crowdsouring solution for time-sensitive cases.
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.
Yao Yu, Shumei Liu, Lei Guo, Phee Lep Yeoh · 6 authors
Mobile crowdsourcing is a promising strategy for trusted data collection in Internet-of-Things (IoT) applications. In this article, we propose a new fog-blockchain distributed approach for crowdsourcing reputation management to prevent user's privacy leakage, malicious users' participation, and reputation tampering in wireless IoT systems. To protect the user's privacy, we design a cross-layer privacy protection model to separate the user's identity and tasks flexibly by means of a hierarchical structure based on fog computing. Moreover, considering the multiconstraint requirement of crowdsourcing tasks, we present a multifactor reputation evaluation method to accurately identify malicious users. Furthermore, to solve the multi-identity problem of users on multiple fog nodes, we propose an adaptive fog-blockchain reputation storage method, which efficiently reduces the system resource consumption by analyzing the adaptive classification of fog nodes. Exhaustive experimental simulation results validate the security and efficiency of our proposed reputation management system.