Electronic Health Record (EHR) and its privacy have attracted widespread attention with the development of the healthcare industry in recent years. As locking medical data in a single healthcare center causes information isolation, healthcare centers are motivated to build medical data sharing systems. However, existing systems highly rely on the trusted centralized servers, which are vulnerable to distributed denial of service (DDoS) attacks and the single point of failure. Moreover, it is a non-trivial matter to authorize multiple users to search and access EHR in a privacy-preserving manner. In this paper, we propose MedShare, a decentralized framework for secure EHR sharing. Our design utilizes the smart contract technique of blockchain to establish a trusted platform for healthcare centers to share their encrypted EHR. Considering that fine-grained access control is essential in practical EHR sharing service, we devise a constant-size attribute-based encryption (ABE) scheme, where the access policy is embedded in search result on the blockchain. Besides, we propose an efficient scheme that enables authorized MedShare users to perform multi-keyword boolean search operations over encrypted EHR. We formally analyze the security strengths and implement the system prototype on Ethereum. Evaluation results demonstrate that MedShare is efficient for EHR sharing.
Darrell Yonathan, Diyanatul Husna, F. Astha Ekadiyanto, I Ketut Eddy Purnama · 10 authors
This paper discusses the implementation of smart contracts on the Ethereum blockchain system for telemedicine data storage. Telemedicine is one of the currently developing digital technologies in the health and medical sectors. Telemedicine can be more efficient when seeking treatment because patients do not need to see a doctor face to face. When using blockchain technology, the stored data becomes more transparent for each node in the blockchain network but has verification on every transaction which takes time and gas costs. However, telemedicine has several risks and problems, one of which is long data storage process time because there must be a verification process first to ensure data security. Another problem faced is the issue of the gas fee of the blockchain telemedicine system which is billed in every data storage transaction. In this study, a blockchain system was introduced for managing and securing databases on telemedicine. The implementation of this blockchain system was carried out on a website page that can add data to and retrieve data from the blockchain system. The results of this study showed that blockchain was successfully implemented to store telemedicine data with Ethereum. The analysis in this paper refers to the set and gets functions. The set function is used to send data to the blockchain, and the get function is used to retrieve data from the blockchain. From testing, the Get function has a much faster execution time than the Set function because the Get function does not require verification to retrieve its data. In the iterations carried out—namely 1, 10, and 100—the longest time on average was at 100 iterations when compared to the other iterations. In the tests carried out, the more characters that were stored, the more gas costs must be paid. In the tests, the percentage increase in costs was 0.34% per character.
There has been a great deal of discussion of the challenges on privacy, data interoperability and quality of Educational Professional Personal Record (EPPR). Therefore, there is a need to reassess the current models, in which various parties generate, exchange and observe a huge amount of personal data with regard to EPPR. Ethereum blockchain has shown that trusted, auditable transactions is detectible using a decentralized network of nodes accompanied by a general ledger. Thus, due to the fast-moving development of educational and professional data generators such as online universities and distance learning, requires learners to engage in detail into their EPPR as well as the educational and professional data generators. In this paper, we propose a novel decentralized framework to manage EPPR using Ethereum blockchain technology. The framework provides the owner of the EPPR a comprehensive immutable log and ease of access to their educational records across the educational record editors and consumers. Furthermore, it provides a recommender engine to endorse skills and competencies to the education record owners and similar candidates for educational records editors and consumers. Ethereum blockchain can provide solutions in terms of exchanging of data among parties by ensuring privacy, accountability and data interoperability. The aim of the proposed framework is to enable educational stakeholders (universities and employing agencies) to participate in the network as blockchain miners rewarded by pseudonymized data in compliance with General Data Protection Rules in United Arab Emirates.
Chaosheng Feng, Keping Yu, Ali Kashif Bashir, Yasser D. Al‐Otaibi · 7 authors
The drone's open and untrusted environment may create problems for authentication and data sharing. To address this issue, we propose a blockchain-enabled efficient and secure data sharing model for 5G flying drones. In this model, blockchain and attribute-based encryption (ABE) are applied to ensure the security of instruction issues and data sharing. The authentication mechanism in the model employs a smart contract for authentication and access control, public key cryptography for providing accounts and ensuring accounts' security, and a distributed ledger for security audit. In addition, to speed up out-sourced computations and reduce electricity consumption, an ABE model with parallel outsourced computation (ABEM-POC) is constructed, and a generic parallel computation method for ABE is proposed. The analysis of the experimental results shows that parallel computation significantly improves the speed of outsourced encryption and decryption compared to serial computation.
Abstract We consider the problem of proving in zero knowledge that an element of a public set satisfies a given property without disclosing the element, i.e., for some u , “ $$u \in S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>u</mml:mi><mml:mo>∈</mml:mo><mml:mi>S</mml:mi></mml:mrow></mml:math> and P ( u ) holds”. This problem arises in many applications (anonymous cryptocurrencies, credentials or whitelists) where, for privacy or anonymity reasons, it is crucial to hide certain data while ensuring properties of such data. We design new modular and efficient constructions for this problem through new commit-and-prove zero-knowledge systems for set membership , i.e. schemes proving $$u \in S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>u</mml:mi><mml:mo>∈</mml:mo><mml:mi>S</mml:mi></mml:mrow></mml:math> for a value u that is in a public commitment $$c_u$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>c</mml:mi><mml:mi>u</mml:mi></mml:msub></mml:math> . We also extend our results to support non-membership proofs , i.e. proving $$u \notin S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>u</mml:mi><mml:mo>∉</mml:mo><mml:mi>S</mml:mi></mml:mrow></mml:math> . Being commit-and-prove, our solutions can act as plug-and-play modules in statements of the form “ $$u \in S$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>u</mml:mi><mml:mo>∈</mml:mo><mml:mi>S</mml:mi></mml:mrow></mml:math> and P ( u ) holds” by combining our set (non-)membership systems with any other commit-and-prove scheme for P ( u ). Also, they work with Pedersen commitments over prime order groups which makes them compatible with popular systems such as Bulletproofs or Groth16. We implemented our schemes as a software library, and tested experimentally their performance. Compared to previous work that achieves similar properties—the clever techniques combining zkSNARKs and Merkle Trees in Zcash—our solutions offer more flexibility, shorter public parameters and $$3.7 \times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>3.7</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math> – $$30\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>30</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math> faster proving time for a set of size $$2^{64}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>2</mml:mn><mml:mn>64</mml:mn></mml:msup></mml:math> .
Blockchain is a distributed accounting system. One of its key issues is how to ensure agreement between distrustful nodes. As an important component of blockchain, consensus algorithm can solve the consistency problem, and its efficiency directly determines the performance of blockchain. Compared with Proof of Work (PoW) and Proof of Stake (PoS), the existing Delegated Proof of Stake (DPoS) consensus algorithm improves the efficiency of consensus, but it will face some threats, such as DoS attack and collusion attack, because the mechanism that each witness node takes turns to generate blocks. Therefore, we propose a delegated proof of stake consensus algorithm with dynamic trust, that is, DT-DPoS. We improve an Eigen Trust-based trust model and construct an evaluation criterion based on the combination of stake voting and trust value, which improves the reliability of witness nodes and reduces the risk of collusion attacks. At the same time, we use a ring signature scheme, which ensures the privacy and anonymity of witness nodes. Finally, the theoretical analysis shows the effectiveness and scalability of our algorithm.
Aiming at the problems of the existing DPoS(Delegated Proof of Stake) consensus algorithm, such as low enthusiasm of voting nodes and difficulties in dealing with malicious nodes, we improve the traditional DPoS consensus algorithm and propose a reputation-based delegated proof of stake consensus algorithm, called Reputation-DPoS. In our Reputation-DPoS, the reputation model is introduced. By evaluating the behavior of nodes, nodes are divided into different trusted states, and high-quality nodes in the network are selected as consensus nodes to reduce security risks and improve efficiency. Besides, incentive methods of reputation and token are used to improve the enthusiasm of nodes to participate in voting. Simulation results show that our Reputation-DPoS can reduce the probability of malicious nodes being selected and optimize the state of nodes in DPoS. Nodes with good behavior will get more votes and rewards, which will motivate nodes and improve the security of the system. Insert here your abstract text.
Shirshak Raja Maskey, Shahriar Badsha, Shamik Sengupta, Ibrahim Khalil
Vehicular edge computing (VEC) is one of the most efficient ways to have high functionality in resource-constrained connected vehicles. As the edge nodes in VEC holds paramount importance and highly sensitive data, we need to make sure the information exchange remains tamper-free. Although blockchain provides immutable and verifiable data storage solutions, the nodes that enable blockchain might be under attack such as a denial of service from various malicious actors. One way to nullify these types of attacks is to find the nodes under attack and remove them from the blockchain consensus process to ensure the proper functioning of the system, i.e., miner node selection. Similarly, in order to provide the decision on which node is to be removed from the network, we need a smart decision-making technique such as an artificial neural network (ANN). The ANN is used to calculate the reputation of the miner nodes. In this article, we propose a reputation-based miner node selection (RbMNS) in blockchain-based vehicular edge computing (BloVEC). We have used ANN and hyperledger fabric (HF) to implement the RbMNS and blockchain model in the system. We obtained the average transactional latency of 128 ms/109 ms with/without our implementation, respectively.
Lei Liu, Jie Feng, Qingqi Pei, Chen Chen · 7 authors
Mobile-edge computing (MEC) plays a significant role in enabling diverse service applications by implementing efficient data sharing. However, the unique characteristics of MEC also bring data privacy and security problem, which impedes the development of MEC. Blockchain is viewed as a promising technology to guarantee the security and traceability of data sharing. Nonetheless, how to integrate blockchain into MEC system is quite challenging because of dynamic characteristics of channel conditions and network loads. To this end, we propose a secure data sharing scheme in the blockchain-enabled MEC system using an asynchronous learning approach in this article. First, a blockchain-enabled secure data sharing framework in the MEC system is presented. Then, we present an adaptive privacy-preserving mechanism according to available system resources and privacy demands of users. Next, an optimization problem of secure data sharing is formulated in the blockchain-enabled MEC system with the aim to maximize the system performance with respect to the decreased energy consumption of MEC system and the increased throughput of blockchain system. Especially, an asynchronous learning approach is employed to solve the formulated problem. The numerical results demonstrate the superiority of our proposed secure data sharing scheme when compared with some popular benchmark algorithms in terms of average throughput, average energy consumption, and reward.
Vehicular crowdsensing has been widely leveraged in the intelligent transportation systems to collect real-time traffic data. However, the data security and privacy preservation issues have not been well investigated for vehicular crowdsensing. This article proposes a blockchain-enabled vehicular crowdsensing system to protect user privacy and data safety in 5G Internet of Vehicles (IoV). In order to maximize the security and minimize the latency of blockchain, a deep reinforcement learning (DRL)-enabled algorithm is proposed to select proper active miners and transactions. Then, a two-sided matching-based algorithm is put forwarded to allocate the nonorthogonal multiple access subchannels to minimize the maximum uploading delay of all users to guarantee the freshness of messages. Extensive experimental results demonstrate the effectiveness of our system. Finally, system analysis demonstrates that our system can protect user privacy, guarantee data security as well as integrity, and defend against common attacks. With the development of information communication techniques, vehicular crowdsensing is widely leveraged in the Intelligent Transportation Systems (ITSs) to collect real-time traffic information (e.g., traffic accidents and jams). Recently, vehicles have begun to be regarded as the consumer electronics product, due to the advancements in the development of autonomous vehicles, and users in vehicular crowdsensing focus more and more on their privacy protection and data security. In order to guarantee the connectivity and communication of users participating in the vehicular crowdsensing, intelligent management of network traffic and offering services to vehicles has become a vital need, especially for the highly dynamic topology and untrusty communication environment in 5G Internet of Vehicles (IoV).1 However, the vehicular crowdsensing-based data collection and transmission in 5G IoVs face the following major challenges:
In 5G and beyond networks, the increasing inclusion of heterogeneous smart devices and the rising privacy and security concerns, are two crucial challenges in terms of computation complexity and privacy preservation for Artificial Intelligence (AI)-based solutions. In this regard, federated learning emerges as a new technique, which enlarges the scale of training data, and protects the privacy of user data. The development of edge computing makes it possible to apply federated learning to beyond 5G. However, the security of local parameters, the learning quality, and the varying computing and communication resources, are crucial issues that remain unexplored in federated learning schemes. In this article, we propose a block-chain empowered federated learning framework, and present its potential application scenarios in beyond 5G. We enhance the security and privacy by integrating blockchain into a federated learning scheme for maintaining the trained parameters. In particular, we formulate the resource sharing task as a combinational optimization problem while taking resource consumption and learning quality into account. We design a deep reinforcement learning based algorithm to find an optimal solution to the problem. Numerical results show that the proposed scheme achieves high accuracy and good convergence.
Vehicular crowdsensing is attracting more and more attention because of its wide sensing coverage and diverse usage in smart cities. However, privacy issues that stem from traditional vehicular crowdsensing scenarios, violate the participant's privacy. Although some privacy-preserving schemes have been designed that aim to protect the sensitive information of sensed data, the reliability cannot be guaranteed because of the system's centralized structure. The introduction of blockchain in crowdsensing applications provides reliable data storage, however, the reliability of data sources remains an open challenge. Under these circumstances, the crowdsourcing service requester may not be able to obtain quality data. To solve these problems, we propose a novel Privacy-preserving and Reliable Vehicular crowdsensing via Blockchain oracle, called PRVB. More specifically, a privacy-preserving vehicular data aggregation scheme is presented to protect the data privacy and unlinkability between participant vehicles and sensed data. Besides, two protocols are designed to protect data privacy and to achieve fair rewards for data providers. Thorough theoretical analysis and experimental evaluations have proved that the proposed PRVB achieves privacy protection, reliability, and fairness with significant computation & communication efficiency.
Abstract Information security has become the focus problem in the Internet of Things, and the traditional centralized access control model is faced with threats such as single point failure, internal attack, and central leak. In this paper, we proposed a model to improve the access control security of the Internet of Things, which is based on zero-knowledge proof and smart contract technology in the blockchain. Firstly, we deployed the attribute information of access control in the blockchain, which relieves the pressure and credibility problem brought by the third-party information concentration; Secondly, the encrypted access control token is used to gain the access permission of the resources, which makes the user's identity invisible and effectively avoids the attribute ownership exposure problem; Besides, the use of smart contracts solves the problem of low computing efficiency of Internet of Things devices and the waste of blockchain computing power resources; Finally, a prototype of Internet of Things access control system based on blockchain and zero-knowledge proof technology is implemented. The test analysis results show that the model achieves effective attribute privacy protection, compared with the Attribute-Based Access Control model of the same security level, the access efficiency increases linearly with the increase of access scale.
Nowadays, the advance of smart grid technology has fostered the development of microgrids, which can efficiently control and manage the distributed energy resources (DERs). In smart grid, IoT devices generate huge amounts of data, which are collected and shared among DERs, microgrids, and the main grid. To protect the shared data, it is necessary to implement the secure and efficient data access control. Ciphertext policy attribute-based encryption (CP-ABE) is a promising solution for the distributed system. However, lightweight IoT devices with limited computing capability cannot handle the computationally intensive ABE algorithms. To overcome this constraint, the decryption phase of CP-ABE is usually outsourced to the cloud, but this is inefficient and not safe enough in the distributed environment. In this article, we propose an edge blockchain empowered secure data access control scheme with fair accountability for the smart grid. The computation workloads of end user devices are outsourced to the edge nodes in a consortium blockchain system We adopt an on-chain/off-chain approach to ensure the flexible data sharing. Additionally, we adopt the threshold secret sharing scheme to establish a distributed authority. The security analysis and performance evaluation are conducted to prove the security and efficiency of our scheme. We use the Raspberry Pi to simulate lightweight IoT devices in the Hyperledger fabric platform to prove the usability of our scheme.