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.
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.
In this paper, the first data trading certification blockchain solution based on consortium blockchain and smart contracts is proposed to solve the certification data security problem in data trading, so as to realize the auditability, accountability and integrity of data trading. By the proof-of-authority algorithm, a cheap-and-quick consortium blockchain is built. By the consortium blockchain, smart contracts can be deployed safely and conveniently. By the Solidity language, a concise-and-effective certification data smart contract and data trading smart contract are constructed to ensure the certification data security. By deploying the certification data smart contract and the data trading smart contract on the consortium blockchain, the security, transparency and supervisability of certification data is carried out. By utilizing the consortium blockchain and smart contracts, the data trading certification model is established to ensure the certification data security in data trading. By the experiments, the consortium blockchain is successfully established, and the certification data smart contract and the data trading smart contract are successfully deployed, so that the certification data security is effectively guaranteed. Finally, by the Github, the source code of the certification data smart contract and the data trading smart contract is uploaded.
For several industrial applications, a sole data owner may lack sufficient training samples to train effective machine learning based models. As such, we propose a federated learning (FL) based approach to promote privacy-preserving collaborative machine learning for applications in smart industries. In our system model, a model owner initiates an FL task involving a group of workers, i.e., data owners, to perform model training on their locally stored data before transmitting the model updates for aggregation. There exists a tradeoff between service latency, i.e., the time taken for the training request to be completed, and age of information (AoI), i.e., the time elapsed between data aggregation from the deployed industrial Internet of Things devices to completion of the FL-based training. On one hand, if the data are collected only upon the model owner's request, the AoI is low. On the other hand, the service latency incurred is more significant. Furthermore, given that different training tasks may have varying AoI requirements, we propose a contract-theoretic task-aware incentive scheme that can be calibrated based on the weighted preferences of the model owner toward AoI and service latency. The performance evaluation validates the incentive compatibility of our contract amid information asymmetry, and shows the flexibility of our proposed scheme toward satisfying varying preferences of AoI and service latency.
Academic credentials are precious assets as they form an evidence for one’s identity and eligibility. Fraud inissuance and verification of academic certificates have been a long-standing issue in academic community. Due to lack of antiforgery mechanisms there has been substantial increase in fraudulent certificates. The need of the hour is to have a transparent and reliable model for issuing and verifying academic certificates to eliminate fraud in the process. Decentralized, Auditable and Tamper-proof properties of Blockchain makes it possibly the best choice for issuing and verifying academic certificates. In this paper we propose a model, where regulatory body authorizes higher education Institutes (universities and colleges) for issuing academic certificates to students in a decentralized way. Anyone in the world can verify the authenticity of the certificate by triggering appropriate smart contract functions, thus eliminating any possibility of fraud in the process. In addition we used multi signature scheme where certificates are required to be signed by designated authority from Higher Education Institutes, thus allowing for multi-level checks on certificate contents before being successfully deployed on Blockchain. We have also provide Proof of Concept in Ethereum Blockchain and evaluated its performance in terms of cost, security and scalability.
In blockchain-based anonymous cryptocurrencies, due to their tamper-resistance and transparency characteristics, transaction data are initially required to be anonymous, with the help of various cryptographic techniques, e.g., commitment schemes and zero-knowledge proofs. Also, cryptocurrencies are different from existing anonymous messaging protocols regarding the software architecture and the underlying security model. Due to these differences, the sense of anonymity must be specifically defined for anonymous cryptocurrencies, and the anonymity in each anonymous cryptocurrency must be analyzed and evaluated based on the specific architecture model. In this paper, we first propose a specific architecture model with three software layers to anonymous cryptocurrencies. Next, we introduce definitions of fundamental privacy properties (Pfitzmann's anonymity, unlinkability, and pseudonymity) and comprehensively analyze each privacy property for each architecture layer of anonymous cryptocurrencies to establish a privacy evaluation policy for anonymous cryptocurrencies. Finally, we fairly compare the privacy of current leading anonymous cryptocurrencies (e.g., Zerocash, CryptoNote, and Mimblewimble) using the privacy evaluation policy.
With the development of social networks, there are more and more social data produced, which usually contain valuable knowledge that can be utilized in many fields, such as commodity recommendation and sentimental analysis. The SVM classifier, as one of the most prevailing machine learning techniques for classification, is a crucial tool for social data analysis. Since training a high-quality SVM classifier usually requires a huge amount of data, it is a better choice for individuals and small enterprises to conduct collaborative training with multiple parties. Nevertheless, it causes privacy risks when sharing sensitive data with untrusted people and enterprises. Existing solutions mainly adopt the computation-intensive cryptographic methods which are not efficient for practical applications. Therefore, it is an urgent and challenging task to realize efficient SVM classifier training while protecting privacy. In this paper, we propose a novel privacy-preserving nonlinear SVM classifier training scheme based on blockchain. We first design a series of secure computation protocols which can achieve secure nonlinear SVM classifier training with minimal computation overheads. Then, leveraging these building blocks, we propose a blockchain-based secure nonlinear SVM classifier training scheme that realizes collaborative training while protecting privacy. We conduct a thorough analysis of the security properties of our scheme. Experiments over a real dataset show that our scheme achieves high accuracy and practical efficiency.
Francisco José de Haro-Olmo, Ángel Jesús Varela‐Vaca, José Antonio Álvarez Bermejo
The research presented aims to investigate the relationship between privacy and anonymisation in blockchain technologies on different fields of application. The study is carried out through a systematic literature review in different databases, obtaining in a first phase of selection 199 publications, of which 28 were selected for data extraction. The results obtained provide a strong relationship between privacy and anonymisation in most of the fields of application of blockchain, as well as a description of the techniques used for this purpose, such as Ring Signature, homomorphic encryption, k-anonymity or data obfuscation. Among the literature researched, some limitations and future lines of research on issues close to blockchain technology in the different fields of application can be detected. As conclusion, we extract the different degrees of application of privacy according to the mechanisms used and different techniques for the implementation of anonymisation, being one of the risks for privacy the traceability of the operations.
Learning from data owned by several parties, as in federated learning, raises challenges regarding the privacy guarantees provided to participants and the correctness of the computation in the presence of malicious parties. We tackle these challenges in the context of distributed averaging, an essential building block of distributed and federated learning. Our first contribution is a novel distributed differentially private protocol which naturally scales with the number of parties. The key idea underlying our protocol is to exchange correlated Gaussian noise along the edges of a network graph, complemented by independent noise added by each party. We analyze the differential privacy guarantees of our protocol and the impact of the graph topology, showing that we can match the accuracy of the trusted curator model even when each party communicates with only a logarithmic number of other parties chosen at random. This is in contrast with protocols in the local model of privacy (with lower accuracy) or based on secure aggregation (where all pairs of users need to exchange messages). Our second contribution is to enable users to prove the correctness of their computations without compromising the efficiency and privacy guarantees of the protocol. Our construction relies on standard cryptographic primitives like commitment schemes and zero knowledge proofs.
Rakib Ul Haque, A S M Touhidul Hasan, Qingshan Jiang, Qiang Qu
Numerous works focus on the data privacy issue of the Internet of Things (IoT) when training a supervised Machine Learning (ML) classifier. Most of the existing solutions assume that the classifier’s training data can be obtained securely from different IoT data providers. The primary concern is data privacy when training a K-Nearest Neighbour (K-NN) classifier with IoT data from various entities. This paper proposes secure K-NN, which provides a privacy-preserving K-NN training over IoT data. It employs Blockchain technology with a partial homomorphic cryptosystem (PHC) known as Paillier in order to protect all participants (i.e., IoT data analyst C and IoT data provider P) data privacy. When C analyzes the IoT data of P, both participants’ privacy issue arises and requires a trusted third party. To protect each candidate’s privacy and remove the dependency on a third-party, we assemble secure building blocks in secure K-NN based on Blockchain technology. Firstly, a protected data-sharing platform is developed among various P, where encrypted IoT data is registered on a shared ledger. Secondly, the secure polynomial operation (SPO), secure biasing operations (SBO), and secure comparison (SC) are designed using the homomorphic property of Paillier. It shows that secure K-NN does not need any trusted third-party at the time of interaction, and rigorous security analysis demonstrates that secure K-NN protects sensitive data privacy for each P and C. The secure K-NN achieved 97.84%, 82.33%, and 76.33% precisions on BCWD, HDD, and DD datasets. The performance of secure K-NN is precisely similar to the general K-NN and outperforms all the previous state of art methods.
Distributed ledgers provide high availability and integrity , making them a key enabler for practical and secure computation of distributed workloads among mutually distrustful parties. Many practical applications also require strong confidentiality , however. This work enhances permissioned and permissionless blockchains with the ability to manage confidential data without forfeiting availability or decentralization. The proposed Calypso architecture addresses two orthogonal challenges confronting modern distributed ledgers: (a) enabling the auditable management of secrets and (b) protecting distributed computations against arbitrage attacks when their results depend on the ordering and secrecy of inputs. Calypso introduces on-chain secrets, a novel abstraction that enforces atomic deposition of an auditable trace whenever users access confidential data. Calypso provides user-controlled consent management that ensures revocation atomicity and accountable anonymity. To enable permissionless deployment, we introduce an incentive scheme and provide users with the option to select their preferred trustees. We evaluated our Calypso prototype with a confidential document-sharing application and a decentralized lottery. Our benchmarks show that transaction-processing latency increases linearly in terms of security (number of trustees) and is in the range of 0.2 to 8 seconds for 16 to 128 trustees.
There has been wide range of applications involving smart home systems for user comfort and accessibility to essential commodities. Users enjoy featured home services supported by the IoT smart devices. These IoT devices are resource-constrained, incapable of securing themselves and can be easily hacked. Edge computing can provide localized computations and storage which can augment such capacity limitations for IoT devices. Furthermore, blockchain has emerged as technology with capabilities to provide secure access and authentication for IoT devices in decentralized manner. In this paper, we propose an authentication scheme which integrate attribute based access control using smart contracts with ERC-20 Token (Ethereum Request For Comments) and edge computing to construct a secure framework for IoT devices in Smart home system. The edge server provide scalability to the system by offloading heavier computation tasks to edge servers. We present system architecture and design and discuss various aspects related to testing and implementation of the smart contracts. We show that our proposed scheme is secure by thoroughly analysing its security goals with respect to confidentiality, integrity and availability. Finally, we conduct a performance evaluation to demonstrate the feasibility and efficiency of the proposed scheme.
Mirko Zichichi, Stefano Ferretti, Gabriele D’Angelo, Victor Rodrı́guez-Doncel
This paper presents an architecture of a Personal Information Management System, in which individuals can define the access to their personal data by means of smart contracts. These smart contracts, running on the Ethereum blockchain, implement access control lists and grant immutability, traceability and verifiability of the references to personal data, which is stored itself in a (possibly distributed) file system. A distributed authorization mechanism is devised, where trust from multiple network nodes is necessary to grant the access to the data. To this aim, two possible alternatives are described: a Secret Sharing scheme and Threshold Proxy Re-Encryption scheme. The performance of these alternatives is experimentally compared in terms of execution time. Threshold Proxy Re- Encryption appears to be faster in different scenarios, in particular when increasing message size, number of nodes and the threshold value, i.e. number of nodes needed to grant the data disclosure.
Data privacy and sharing has always been a critical issue when trying to\nbuild complex deep learning-based systems to model data. Facilitation of a\ndecentralized approach that could take benefit from data across multiple nodes\nwhile not needing to merge their data contents physically has been an area of\nactive research. In this paper, we present a solution to benefit from a\ndistributed data setup in the case of training deep learning architectures by\nmaking use of a smart contract system. Specifically, we propose a mechanism\nthat aggregates together the intermediate representations obtained from local\nANN models over a blockchain. Training of local models takes place on their\nrespective data. The intermediate representations derived from them, when\ncombined and trained together on the host node, helps to get a more accurate\nsystem. While federated learning primarily deals with the same features of data\nwhere the number of samples being distributed on multiple nodes, here we are\ndealing with the same number of samples but with their features being\ndistributed on multiple nodes. We consider the task of bank loan prediction\nwherein the personal details of an individual and their bank-specific details\nmay not be available at the same place. Our aggregation mechanism helps to\ntrain a model on such existing distributed data without having to share and\nconcatenate together the actual data values. The obtained performance, which is\nbetter than that of individual nodes, and is at par with that of a centralized\ndata setup makes a strong case for extending our technique across other\narchitectures and tasks. The solution finds its application in organizations\nthat want to train deep learning models on vertically partitioned data.\n
<title>Abstract</title> Data is the most important factor in building a smart city. City data is composed of many data islands, such as transportation, industry, and residents. In order to build a smart city, breaking data islands, achieving trusted and collaborative sharing of data, while protecting data privacy are essential. As a distributed ledger, the blockchain can solve the problem of data trust. Federated learning achieves data privacy protection by sharing model parameters instead of original data. However, it still has some problems such as malicious nodes and differential attacks. This paper proposes a data sharing mechanism that combines blockchain and federated learning over smart city. Firstly, the blockchain is combined to ensure the credibility of the performance information of the work nodes, then the work node selection algorithm is designed, and a consensus incentive mechanism IPoQ is proposed for efficient federated learning tasks. Finally, differential privacy technology is introduced to resist differential attack. Experimental results show that the methods proposed in this paper achieves an effective federated learning data sharing mechanism.
Abstract Even though there is continuous improvement in road and vehicle safety, road traffic incidents have been increasing over last few decades. There is a need to reduce traffic incidents like accidents through predictive analysis and timely warnings while at the same time data related to accidents and traffic violations need to be maintained in a tamper proof storage system that can be retrieved for forensic analysis and law enforcement at a later stage. The Secure Incident and Evidence Management Framework (SIEMF) proposed in this work address these two challenges of predictive modeling for timely warning and secure evidence management for forensics analysis in case of accidents and traffic violations. The system proposes a deep learning based predictive incident modeling with blockchain and CP-ABE based access control for the incident data stored in blockchain.
Today healthcare industries are maintaining COVID-19 patients' information electronically which includes patients' diagnostic reports, patients' private information, and doctor prescriptions. However, the COVID-19, patient sensitive information is currently stored in centralized or third-party storage model. One of the key challenge of centralized storage model is the preserving privacy of patient information and transparency in the system. The privacy risk include illegitimate access to sensitive information of patient such as identification details access and misutilization of patient information and their clinical records. To overcome this challenge, we proposed a distributed on-chain and off-chain storage model using consortium blockchain and interplanetary file systems (IPFS). The proposed framework though maintaining patient privacy makes it easier for legitimate entities like healthcare providers (e.g., physicians and clinical staffs) to access clinical data of COVID-19 patients'.
COVID-19 is a major global public health challenge and difficult to control in a short time completely. To prevent the COVID-19 epidemic from continuing to worsen, global scientific research institutions have actively carried out studies on COVID-19, thereby effectively improving the prevention, monitoring, tracking, control, and treatment of the epidemic. However, the COVID-19 electronic medical records (CEMRs) among hospitals worldwide are managed independently. With privacy consideration, CEMRs cannot be made public or shared, which is not conducive to in-depth and extensive research on COVID-19 by medical research institutions. In addition, even if new research results are developed, the disclosure and sharing process is slow. To address this issue, we propose a blockchain-based medical research support platform, which can provide efficient and privacy-preserving data sharing against COVID-19. First, hospitals and medical research institutions are treated as nodes on the alliance chain, so consensus and data sharing among the nodes is achieved. Then, COVID-19 patients, doctors, and researchers need to be authenticated in various institutes. Moreover, doctors and researchers need to be registered with the Fabric certificate authority. The CEMRs for COVID-19 patients uses the blockchain's pseudonym mechanism to protect privacy. After that, doctors upload CEMRs on the alliance chain, and researchers can obtain CEMRs from the alliance chain for research. Finally, the research results will be published on the blockchain for doctors to use. The experimental results show that the read and write performance and security performance on the alliance chain meet the requirements, which can promote the wide application of scientific research results against COVID-19.
We propose Agora, the first privacy-aware data marketplace that enables parties to get compensated for contributing data, without relying on a trusted third party. We leverage cryptographic techniques to achieve three security properties: (i) data privacy-raw data remain private except for a function output, (ii) output verifiability-the output is proven to be correct, and (iii) atomicity of payments-parties cannot avoid paying for provided services. Agora is designed as a decentralized blockchain application via smart contracts. We implement a prototype on Ethereum and evaluate its performance in terms of computation overhead and monetary cost.
Blockchain is an especially promising and revolutionary technology that brings transparency in a scalable way for multiple organizations and this is thanks to its several features. There are some exciting blockchain features but among them, decentralization is undoubtedly the most interesting one. Organizations can share data within a distributed ledger. As a consequence, each one can access synchronized data stored in its local Blockchain node. This functionality improves transaction tracking and facilitates access to data within a private group of organizations. However, in some cases, even if organizations accept to share data, they require to hide some private information related to their users or their business model. To tackle privacy and trust issues between organizations, this paper presents a blockchain architecture based on the proxy re-encryption scheme. This scheme is integrated within smart contracts to provide a very efficient, fast, and secure platform. The proposed architecture is implemented in an Hyperledger Blockchain and tested in a real transport and mobility use case.
Nida Khan, Abdelkader Lahmadi, Zsófia Kräussl, Radu State
Blockchain has emerged as a novel solution addressing a plethora of industrial issues in domains spanning from financial to educational. However, several challenges restrict the widespread adoption of the technology and data privacy, with throughput and scalability issues, ranks amongst the foremost. In this paper, we introduce a novel privacy management plane which integrates differential privacy to query existing relational databases through the blockchain as well as spearheads the use of blockchain for local differential privacy. The distinguishing feature in the latter is that the privacy management plane gives the data owners the right to perturb their data with the desired privacy budget, while in the former it gives the right to the data curator to change the privacy budget dynamically while answering queries through the blockchain. The paper also includes experimental evaluation of the developed privacy management plane and integrates management operations in it through another smart contract. The paper addresses the issue of GDPR and it's implications in the context of blockchain data, while highlighting the compliance of the proposed implementation.
Dara Tith, Joong-Sun Lee, H. Suzuki, W. M. A. B. Wijesundara · 7 authors
OBJECTIVES: Currently, patients' consent is essential to use their medical records for various purposes; however, most people give their consent using paper forms and have no control over it. Healthcare organizations also have difficulties in dealing with patient consent. The objective of this research is to develop a system for patients to manage their consent flexibly and for healthcare organizations to obtain patient consent efficiently for a variety of purposes. METHODS: We introduce a new e-consent model, which uses a purpose-based access control scheme; it is implemented by a blockchain system using Hyperledger Fabric. All metadata of patient records, consents, and data access are written immutably on the blockchain and shared among participant organizations. We also created a blockchain chaincode that performs business logic managing patient consent. RESULTS: We developed a prototype and checked business logics with the chaincode by validating doctors' data access with purpose-based consent of patients stored in the blockchain. The results demonstrate that our system provides a fine-grained way of handling medical staff 's access requests with diverse intended purposes for accessing data. In addition, patients can create, update, and withdraw their consents in the blockchain. CONCLUSIONS: Our consent model is a solution for consent management both for patients and healthcare organizations. Our system, as a blockchain-based solution that provides high reliability and availability with transparency and traceability, is expected to be used not only for patient data sharing in hospitals, but also for data donation for biobank research purposes.
The problem of fair exchange consists of interchanging goods between two parties that do not trust each other. Despite known impossibility results, recent works leverage the block-chain and zero-knowledge proofs to implement zero-knowledge contingent payment (zkCP) systems that make fair exchange of digital goods possible. Implementing these systems in a secure and efficient way is a big challenge, as evidenced by several unsuccessful attempts from the literature. Campanelli et al. (ACM CCS 2017) discovered a vulnerability on an existing zkCP proposal based on SNARKs (succinct non-interactive arguments of knowledge) and suggested several repairs. Fuchsbauer (ACM CCS 2019) found a flaw in the mentioned countermeasures. In particular, he showed that witness-indistinguishability (WI) is not sufficient for the zkCP schemes proposed by Campanelli et al. to be secure. In this work, we observe that a slightly stronger notion of WI, that we coin trapdoor subversion WI (tS-WI), rules out Fuchsbauer's attack. We formally define security properties for CP systems and show that, under tS-WI, Campanelli et al.'s proposal indeed satisfies these properties. Additionally, we explore alternative approaches to implement ZK (other than SNARKs) and develop a prototype, using it to demonstrate their potential. Our new ideas result in a protocol to sell ECDSA signatures with contingent payment that can be executed in less than $150$ milliseconds over a LAN network.