Information exchange between tax administrations, businesses, and auditors is key to effective tax enforcement. Therefore, organizations proposed the application of blockchain technology to interconnect the different actors and increase tax transparency. However, the lack of confidentiality measures hampers further development. Especially, businesses are concerned about the disclosure of commercially sensitive information that might threaten their competitive advantage. In this paper, we investigate how the application of zero-knowledge-proofs can contribute to solving the dilemma between transparency and confidentiality in blockchain-based tax systems. To meet this end, we provide a conceptual design of a confidentiality-preserving distributed tax ledger. Moreover, we present a prototype addressing reporting obligations in the context of value-added tax. Our evaluation shows that zero-knowledge proofs are an effective measure to trade off transparency against confidentiality. Still, their application is challenging and future research must focus on better abstractions of proving statements.
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.
Double auction is an auction in which multiple buyers and sellers looking for a price where supply and demand balance. Since the electronic double auction based on secure multiparty computation (DABSMPC) cannot guarantee its fairness, we propose a blockchain-based fair and secure electronic double auction protocol (BFSDA). BFSDA modifies the data input and distribution mechanism of participants in DABSMPC, which improves the security of the protocol. Then, the BFSDA introduces and improves the blockchain-based fair and secure multiparty computation protocol (BFSMPC) to ensure fairness while increasing the success rate of secret recovery. In addition, BFSDA uses a fairer and more efficient protocol for secure two-party comparing to obtain the final marketing clearing price. The schema analysis result of BFSDA shows that: first, the private input data will not be revealed as long as data owner is not compromised, second, honest participants can get the result or economic compensation, and third, participants only need to pay the deposit once and a large amount of complicated verification operations are carried out off the chain, which ensures the efficiency of the protocol.
A blockchain, such as Bitcoin, is an append-only, secure, transparent, distributed ledger. A fair blockchain is expected to have healthy metrics; high honest mining power, low processing latency, i.e., low wait times for transactions and stable price of consumption, i.e., the minimum transaction fee required to have a transaction processed. As Bitcoin matures, the influx of transactions increases and the block rewards become insignificant. We show that under these conditions, it becomes hard to maintain the health of the blockchain. In Bitcoin, under these mature operating conditions (MOC), the miners would find it challenging to cover their mining costs as there would be no more revenue from merely mining a block. It may cause miners not to continue mining, threatening the blockchain's security. Further, as we show in this paper using simulations, the cost of acting in favor of the health of the blockchain, under MOC, is very high in Bitcoin, causing all miners to process transactions greedily. It leads to stranded transactions, i.e., transactions offering low transaction fees, experiencing unreasonably high processing latency. To make matters worse, a compounding effect of these stranded transactions is the rising price of consumption. Such phenomena not only induce unfairness as experienced by the miners and the users but also deteriorate the health of the blockchain. We propose BitcoinF transaction processing protocol, a simple, yet highly effective modification to the existing Bitcoin protocol to fix these issues of unfairness. BitcoinF resolves these issues of unfairness while preserving the ability of the users to express urgency and have their transactions prioritized.
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.
S. Velliangiri, G. Krishna Lava Kumar, P. Karthikeyan
Blockchain is extensively used for transaction management for digital assets. Vehicle assets transfer is a challenging task. Several participants are involved in the vehicle assets transfer, so it leads to the risk of modifying the vehicle information, disclosing relevant information to the public, and several faults. In such a situation, necessary data can get very powerless against fakes and information altering or even become non-discernible. Here the attempt is to entail an unsupervised confidential information management system assuring the users possessing and governing their information. The protocol is implemented, which converts the blockchain into computerized access governance manager that does not possibly need to believe the third party. Distinct from bitcoins, the transactions are not purely financial as they usually carry procedures like storing, querying, and distribution of information.
Dominik Schmelz, Karl Pinter, Johannes Brottrager, Phillip Niemeier · 6 authors
The European Union's General Data Protection Regulation (GDPR) has been effective for more than a year. Even though several million euros have been spent on GDPR projects, companies are insecure about being fully compliant. The status quo is that companies lack processes and infrastructure for several legal responsibilities regarding rights of data subjects. This leads to manual effort and long waiting times for users. Data protection authorities receive complaints about these waiting times, but affected people cannot legally submit a proof for request initiation since these requests are usually done via the companies' platforms or email. This paper presents a technical solution to this problem by installing a blockchain-based application to submit and track requests for data access securely whilst preserving data protection for the subjects to be able to file complaints and ultimately ensure the given data protection rights.
Initiatives leveraging the emerging SMART on FHIR standard are promising healthcare system improvements while reducing information technology costs with reusable system components. Previously closed Electronic Medical Record systems are gradually opened to third party applications through FHIR-enabled APIs. While this allows for technical interoperability, patient safety concerns from data quality issues with the underlying system data remain unsolved. We propose to apply a “Data Quality by Contract” approach to pre- and post-conditions of data use cases to assure safe operation of SMART on FHIR apps. We demonstrate how a cardiac risk scoring app could leverage data quality probes to validate several data quality concerns.
Yongfeng Qian, Yingying Jiang, Long Hu, M. Shamim Hossain · 6 authors
The Cognitive Internet of Vehicles (CIoV) introduces a cognitive engine in the traditional Internet of Vehicles, which can realize more intelligent functions such as vehicle deployment and resource allocation. Especially in terms of content caching, the cognitive engine can perceive the content requirements of users and match the content providers and content requesters to improve the caching hit rate. However, during this process, users may worry that their privacy data may be leaked. Content requesters need to submit data about points of interest in contents, which is part of their sensitive information. In addition, in terms of rapid speed of vehicles, the connection time is limited, such as vehicle-to-vehicle and vehicle-to-roadside unit (RSU), which leads to limited transaction time during obtaining contents. In order to solve these problems, in this article, we propose a blockchain-based privacy-aware content caching architecture in CIoV. In general, when vehicles need contents, in order to protect the privacy of vehicles, it is no longer necessary to submit a request to an RSU, but through broadcasting contents from an RSU or surrounding vehicles, where vehicles can selectively obtain contents. To improve the cache hit rate, the cognitive engine will perceive content requirements and recommend relevant content requirements to an RSU or content-providing vehicles based on machine learning or deep learning methods. However, this method of content acquisition will connect different vehicles, which brings the untrusted problem. Both content requesters and providers may worry about untrusted users connected with them. To this end, we adopt the blockchain technology to record the completed content transactions, which are written into the block after the consensus mechanism is completed, thus solving the problem of distrust between vehicles. Experiments demonstrate the privacy-aware content caching architecture based on blockchains effectiveness.
Vehicular fog computing (VFC) has been envisioned as an important application of fog computing in vehicular networks. Parked vehicles with embedded computation resources could be exploited as a supplement for VFC. They cooperate with fog servers to process offloading requests at the vehicular network edge, leading to a new paradigm called parked vehicle assisted fog computing (PVFC). However, each coin has two sides. There is a follow-up challenging issue in the distributed and trustless computing environment. The centralized computation offloading without tamper-proof audit causes security threats. It could not guard against false-reporting, free-riding behaviors, spoofing attacks and repudiation attacks. Thus, we leverage the blockchain technology to achieve decentralized PVFC. Request posting, workload undertaking, task evaluation and reward assignment are organized and validated automatically through smart contract executions. Network activities in computation offloading become transparent, verifiable and traceable to eliminate security risks. To this end, we introduce network entities and design interactive smart contract operations across them. The optimal smart contract design problem is formulated and solved within the Stackelberg game framework to minimize the total payments for users. Security analysis and extensive numerical results are provided to demonstrate that our scheme has high security and efficiency guarantee.
Yang Xu, Cheng Zhang, Quanrun Zeng, Guojun Wang · 6 authors
The emergence of 5 G technology contributes to create more open and efficient eco-systems for various vertical industries. Especially, it significantly improves the capabilities of the vertical industries focusing on content-sharing services like mobile telemedicine, etc. However, cyber threats such as information leakage or piracy are more likely to occur in an open 5 G networks. So tracking information leakage in 5 G environments has become a daunting task. The existing tracing and accountability schemes have nonnegligible limitations in practice due to the dependence on a Trusted Third Party (TTP) or being encumbered with the significant overhead. Fortunately, the blockchain helps to mitigate these problems. In this paper, we propose a blockchain-enabled accountability mechanism against information leakage in the content-sharing services of the vertical industry services. For any information converted to vector form, we use the blockchain technology to ensure that service providers and clients can securely and fairly generate and share watermarked content. Besides, the homomorphic encryption is introduced to avoid the disclosure of the watermarking content, which guarantees the subsequent TTP-free arbitration. Finally, we theoretically analyze the security of the scheme and verify its performance.
The global Electronic Health Record (EHR) market is growing dramatically and has already hit $31.5 billion in 2018. To safeguard the security of EHR data and privacy of patients, fine-grained information access and sharing mechanisms are essential for EHR management. This paper proposes a hybrid architecture of blockchain and edge nodes to facilitate EHR management. In this architecture, we utilize attribute-based multi-signature (ABMS) scheme to authenticate user's signatures without revealing the sensitive information and multi-authority attribute-based encryption (ABE) scheme to encrypt EHR data which is stored on the edge node. We develop the blockchain module on Hyperledger Fabric platform and the ABMS module on Hyperledger Ursa library. We measure the signing and verifying time of the ABMS scheme under different settings, and experiment with the authentication events and access activities which are logged as transactions in blockchain.
Tsung-Ting Kuo, Rodney A. Gabriel, Krishna R. Cidambi, Lucila Ohno‐Machado
OBJECTIVE: Predicting patient outcomes using healthcare/genomics data is an increasingly popular/important area. However, some diseases are rare and require data from multiple institutions to construct generalizable models. To address institutional data protection policies, many distributed methods keep the data locally but rely on a central server for coordination, which introduces risks such as a single point of failure. We focus on providing an alternative based on a decentralized approach. We introduce the idea using blockchain technology for this purpose, with a brief description of its own potential advantages/disadvantages. MATERIALS AND METHODS: We explain how our proposed EXpectation Propagation LOgistic REgRession on Permissioned blockCHAIN (ExplorerChain) can achieve the same results when compared to a distributed model that uses a central server on 3 healthcare/genomic datasets, and what trade-offs need to be considered when using centralized/decentralized methods. We explain how the use of blockchain technology can help decrease some of the problems encountered in decentralized methods. RESULTS: We showed that the discrimination power of ExplorerChain can be statistically similar to its counterpart central server-based algorithm. While ExplorerChain inherited some benefits of blockchain, it had a small increased running time. DISCUSSION: ExplorerChain has the same prerequisites as a distributed model with a centralized server for coordination. In a manner similar to secure multi-party computation strategies, it assumes that participating institutions are honest, but "curious." CONCLUSION: When evaluated on relatively small datasets, results suggest that ExplorerChain, which combines artificial intelligence and blockchain technologies, performs as well as a central server-based method, and may avoid some risks at the cost of efficiency.
High availability of data is responsible for the current trends in Artificial\nIntelligence (AI) and Machine Learning (ML). However, high-grade datasets are\nreluctantly shared between actors because of lacking trust and fear of losing\ncontrol. Provenance tracing systems are a possible measure to build trust by\nimproving transparency. Especially the tracing of AI assets along complete AI\nvalue chains bears various challenges such as trust, privacy, confidentiality,\ntraceability, and fair remuneration. In this paper we design a graph-based\nprovenance model for AI assets and their relations within an AI value chain.\nMoreover, we propose a protocol to exchange AI assets securely to selected\nparties. The provenance model and exchange protocol are then combined and\nimplemented as a smart contract on a permission-less blockchain. We show how\nthe smart contract enables the tracing of AI assets in an existing industry use\ncase while solving all challenges. Consequently, our smart contract helps to\nincrease traceability and transparency, encourages trust between actors and\nthus fosters collaboration between them.\n
Blockchain can be utilized to enhance both security and efficiency for location-based service (LBS) in vehicular social networks (VSNs), due to its inherent decentralization, anonymity, and trust properties. Unfortunately, the existing approaches either lack effective authentication, which is vulnerable to the man-in-the-middle attack, or require an online certificate authority (CA) where frequent interactions with resource-constrained vehicles are required. To address these challenging issues, in this article, a lightweight threshold CA for consortium blockchain along with a privacy-preserving LBS protocol in blockchain enforced VSNs is proposed. First, a lightweight threshold CA framework LTCA is proposed by devising a threshold proxy signature, where the proxy signing key is issued by a coalition of threshold number of CAs playing the roles of authorized nodes in the consortium blockchain. Without the intervene of an online CA, each vehicle in the online phase can authenticate its identity by itself each time its blockchain address (i.e., account address) is updated. Then, based on the proposed LTCA, an efficient privacy-preserving LBS protocol PPVC is contrived to protect each vehicle’s conditional identity privacy with a moderate cost. Finally, both security analysis and performance evaluation demonstrate the effectiveness and efficiency of our proposed LTCA and PPVC in blockchain enforced VSNs.
Yuchuan Fu, F. Richard Yu, Changle Li, Tom H. Luan · 5 authors
The accuracy of the ML model is essential for the further development of AI-enabled CAVs. With the increasing complexity of on-board sensor systems, the large amount of raw data available for learning can however cause big communication burdens and data security issues. To alleviate the communication cost yet improve the accuracy of machine learning with preserved data privacy is an important issue to address in CAVs. In this article, we survey the existing literature toward efficient and secured learning in a dynamic wireless environment. In particular, a BCL framework for AI-enabled CAVs is presented. The framework enables distributed CAVs to train ML models locally and upload to blockchain network to overall utilize the "collective intelligence" of CAVs while avoiding large amounts of data transmission. Blockchain is then applied to protect the distributed learned models. We evaluate the performance of the presented framework by simulations and discuss a range of open research issues that need to be addressed in the future.
M.A.P. Chamikara, Péter Bertök, Ibrahim Khalil, Dongxi Liu · 6 authors
Industrial Internet of Things (IIoT) is revolutionizing many leading industries such as energy, agriculture, mining, transportation, and healthcare. IIoT is a major driving force for Industry 4.0, which heavily utilizes machine learning (ML) to capitalize on the massive interconnection and large volumes of IIoT data. However, ML models that are trained on sensitive data tend to leak privacy to adversarial attacks, limiting its full potential in Industry 4.0. This article introduces a framework named PriModChain that enforces privacy and trustworthiness on IIoT data by amalgamating differential privacy, federated ML, Ethereum blockchain, and smart contracts. The feasibility of PriModChain in terms of privacy, security, reliability, safety, and resilience is evaluated using simulations developed in Python with socket programming on a general-purpose computer. We used Ganache_v2.0.1 local test network for the local experiments and Kovan test network for the public blockchain testing. We verify the proposed security protocol using Scyther_v1.1.3 protocol verifier.
Mobile edge computing (MEC) is to process, analyse, store and calculate the network data at the edge of the network. When the ground infrastructure is damaged in an emergency, the unmanned aerial vehicle (UAV) formation can be rapidly deployed to undertake the task of MEC. However, there are some potential problems to be considered in UAV‐enabled MEC, such as the trust among UAVs from different sources and the stability of UAV formation network. In view of the problems existing, this study proposes a blockchain‐based architecture to build a system of mutual trust, fairness, openness, and stability in this scenario. Through the implementation of blockchain technology, key data such as device computing capacity, task allocation, and task execution process are recorded openly, transparently, and irrevocably. As multi‐party trust is built to reduce the occurrence of fraud, system participants can get a reasonable reward. On this basis, the smart contract is used to ensure that algorithms are accessible to the public, and the sub‐blockchain technology improves the stability of the system. In the case study, the simulation results show that the resource consumption and time cost of the proposed scheme is reasonable and feasible.
In Internet of Vehicles (IoV), data sharing among vehicles for collaborative analysis can improve the driving experience and service quality. However, the bandwidth, security and privacy issues hinder data providers from participating in the data sharing process. In addition, due to the intermittent and unreliable communications in IoV, the reliability and efficiency of data sharing need to be further enhanced. In this paper, we propose a new architecture based on federated learning to relieve transmission load and address privacy concerns of providers. To enhance the security and reliability of model parameters, we develop a hybrid blockchain architecture which consists of the permissioned blockchain and the local Directed Acyclic Graph (DAG). Moreover, we propose an asynchronous federated learning scheme by adopting Deep Reinforcement Learning (DRL) for node selection to improve the efficiency. The reliability of shared data is also guaranteed by integrating learned models into blockchain and executing a two-stage verification. Numerical results show that the proposed data sharing scheme provides both higher learning accuracy and faster convergence.
Blockchain has been gradually applied to different Internet-of-Things platforms. As the efficiency of the blockchain mainly depends on the network computing capability, how to make sure the acquisition of the computational resources and participation of the devices would be the driving force. In this article, we focus on investigating incentive mechanism for rational miners to purchase the computational resources. An edge-computing-based blockchain network is considered, where the edge service provider (ESP) can provide computational resources for the miners. Accordingly, we formulate a two-stage Stackelberg game between the miners and ESP. The aim is to investigate SE of the optimal mining strategy under the two different mining schemes, in order to find the optimal incentive for the ESP and miners to choose autofit strategies. Through theoretical analysis and numerical simulations, we can demonstrate the effectiveness of the proposed scheme on encouraging devices to participate the blockchain.
Guangsheng Yu, Xuan F. Zha, Xu Wang, Wei Ni · 9 authors
The attribute-based encryption (ABE) has drawn a lot of attention for fine-grained access control in blockchains, especially in blockchain-enabled tampering-resistant Internet-of-Things (IoT) systems. However, its adoption has been severely hindered by the incompatibility between the immutability of typical blockchains and the attribute updates/revocations of ABE. In this article, we propose a new blockchain-based IoT system, which is compatible with the ABE technique, and fine-grained access control is implemented with the attribute update enabled by integrating Chameleon Hash algorithms into the blockchains. We design and implement a new verification scheme over a multilayer blockchain architecture to guarantee the tamper resistance against malicious and abusive tampering. The system can provide an update-oriented access control, where historical on-chain data can only be accessible to new members and inaccessible to the revoked members. This is distinctively different from existing solutions, which are threatened by data leakage toward the revoked members. We also provide analysis and simulations showing that our system outperforms other solutions in terms of overhead, searching complexity, security, and compatibility.
Proof-of-vote is a third generation of the Nakamoto consensus. With proof-of-vote, validators compete for people-votes, using proof-of-suffrage given by proof-of-person, and authorize transactions based on authority delegated by the consensus mechanism, just like proof-of-work or proof-of-stake. This logical conclusion of the Nakamoto consensus allows a “nation” of people to secure their own ledger, the equivalent of representative democracy for distributed ledger technology.