In the vehicular ad hoc networks (VANETs), vehicles share content with other vehicles and roadside units (RSU) to improve traffic efficiency. However, the vehicles and RSUs are not always credible. If they have malicious behavior, sharing false information put lives in danger. To address these security challenges, we propose a content sharing management method based on blockchain in VANETs. Specifically, we propose a hybrid trust model to evaluate the credibility of content based on the vehicle entity and interactive data. We deploy practical Byzantine fault tolerates (PBFT) consensus protocol based on the interaction frequency between RSUs and vehicles. The higher the interaction frequency, the more likely the RSU is to gain the right to package the block. In this way, RSUs and vehicles actively participate in the network, and achieve the content sharing honestly and effectively. We conduct extensive experiments, which demonstrate the implementation feasibility of proposed mechanisms.
Lodovico Giaretta, Ioannis Savvidis, Thomas Marchioro, Šarūnas Girdzijauskas · 7 authors
We envision PDS<sup>2</sup>, a decentralized data marketplace in which consumers submit their tasks to be run within the platform, on the data of willing providers. The goal of PDS2is to ensure that users maintain full control on their data and do not compromise their privacy, while being rewarded for the value that their data generates. In order to achieve this, our marketplace architecture employs blockchain technology, privacy-preserving computation and decentralized machine learning. We then compare different potential solutions and identify the Ethereum blockchain, trusted execution environments and gossip learning as the most suitable for the implementation of PDS<sup>2</sup>. We also discuss the main open challenges that are left to tackle and possible directions for future work.
Yang Gao, Wenjun Wu, Pengbo Si, Zhaoxin Yang · 5 authors
Driven by the extensively emerging applications requiring big data processing, a series of heterogeneous network architectures have been proposed to meet user experience requirements. Among them, the concept of fog computing facilitates the effective integration and utilization of ubiquitous computing resources. In fog computing scenarios, willingness and service billing issues become significant to computing resource sharing and transactions. In this article, the recently developed blockchain technology characterized by successfully enabling consensus in an untrustworthy environment is introduced. Based on the blockchain technology, we propose a new architecture for resource sharing and transactions in fog computing networks, named Blockchain-Enabled Resource Sharing and Transactions in Fog Computing (B-ReST). The physical architecture, functional architecture, and workflow in B-ReST are defined. We also discuss the key technologies in B-ReST such as the smart contracts, the consensus mechanism and the requester and provider matching (RPM). The wireless characteristics of fog computing and blockchain technology are closely combined to make full and efficient use of ubiquitous computing resources. To prove the feasibility of the proposed architecture, the RPM problem is solved by a deep reinforcement learning (DRL) based method. Simulation results show the advantages of B-ReST to realize resource sharing and transactions, and the performance of B-ReST based on the DRL method has been enhanced. Challenges and future research directions are summarized as well.
In order to solve the problems of low credibility, information missing, and low efficiency in the current agricultural product traceability system due to the centralized storage of data, a blockchain-based agricultural product quality and safety traceability platform has been established. This system chose Hyperledger as the implementation of the blockchain and configured multiple organizational nodes. The chain codes have been written in Go language, while the server and client programs have been written in Java., traceability chain is formed based on the sequential relationship of links, and the operation information of each link is recorded in detail, and different departments of different enterprises can participate in the same traceability chain at the same time. The traceability information of agricultural products can be uploaded to the distributed ledger in real time. It has been tested in practice that the platform has been launched and operated stably, all traceability information including production information, storage information, circulation information and sales information of agricultural products can be obtained through client query. This platform could be applied to a variety of agricultural products and ensure the credibility and integrity of the traceability results by the distributed storage of key traceability information.
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
5G and beyond (B5G) networks are leading a digital revolution in telecommunication in both academia and industry. It brings new paradigms in many aspects of people's daily lives due to its advantages. However, it still leaves some issues in terms of security and privacy as challenges. Blockchain, the public database, is an alternative to the traditional centralized systems, serving as the backbone technique in many systems, including manufacturing, economics, and industry. Blockchain is promising in solving the security issues in the sense that it provides desirable properties including decentralization, transparency, immutability, and so on. In this article, we investigate typical security and privacy issues in edge intelligence in B5G networks and devise a framework to integrate blockchain with such systems, which can provide guaranteed security as well as privacy. We also illustrate several possible solutions to these security and privacy issues in edge intelligence in B5G systems based on blockchain and Ethereum to show how blockchain contributes to the coming B5G networks.
Financial Regulation is a form of compliance system that subjects financial institutions to certain requirements and restrictions. Investment Compliance is an example that involves investment restrictions and monitoring on behalf of investors. Hedge Funds differ from other traditional funds such as mutual funds because of their ability to employ complex investment and hedging techniques. These are private entities with few public disclosure requirements. This is useful in a way as the strategies used are confidential which allows financial agents to participate in the financial markets without any fear of information leakage, hence promoting liquidity. However, this is often implied as a lack of transparency. Hedge Funds are expected to produce higher returns, but sometimes investors seek a risk guarantee in addition to higher returns. However, too much transparency rules out the incentives financial entities have by participating in the first place. On the other hand, too much secrecy may give rise to malicious entities that can break the rules due to a lack of compliance. We aim to solve this problem of protecting investors while ensuring the privacy of financial bodies using zero knowledge proofs. Proofs can be visualized as a way of providing enough information to investors while the zero-knowledge property of proofs maintains the privacy of the fund manager’s strategies. We propose a protocol to address this scenario using Zokrates, a framework for verifiable computation using Zk-SNARKs on Ethereum, to encode the constraints and export the verifier. Based on our implementation and analysis, it can be concluded that zero knowledge proofs provide us with a variety of ways to develop compliance systems.
Riaz Ahmad Ziar, Syed Irfanullah, Wajid Ullah Khan, Abdus Salam
Blockchain technology provides several suitable characteristics such as immutability, decentralization and verifiable ledger. It records the transactions in a decentralized way and can be integrated into several fields like eHealth, e-Government and smart cities etc. However, blockchain has several privacy and security issues, one of them is the on-chain data privacy. To deal with this issue we provide a privacy-preserving solution for permission less blockchain to empower the user to take control of transaction data in the open ledger. This work focuses on designing and developing the peer-to-peer system using symmetric cryptography and ethereum smart contract. In this scheme, we create smart contracts for the interaction of the data provider, data consumer, and access control list. Data providers register authorized users in the access control list. Data consumers can check their validity in the access control list. After successful validation, data consumers can request the security key from data providers to access secret information. Based on successful validation, a smart contract that is created between the data provider and data consumer is executed to send a key to the data consumer for accessing the secret information. The smart contracts of this proposed model are modeled in solidity, and the performance of the contracts is assessed in the Ropsten test network.
Marc Jayson Baucas, S. Andrew Gadsden, Petros Spachos
Internet of Things (IoT)-based smart home applications are rising in popularity. However, this trend attracts malicious activity, which causes cost-efficient security to be in high demand. This letter proposes a low-end design that reinforces the security of a home network. It uses private blockchain technology and localization via RSSI-based trilateration. We investigated the benefits of private blockchains over their public counterpart, and we improve the precision of the localization algorithm by testing it against different wireless technologies. The results conclude that using a private blockchain with a WiFi-based communication system produces the most efficient iteration of the proposed design.
Danxin Wang, Lan Zhang, Chuanhe Huang, Xieyang Shen
Blockchain-based trust management has attracted great attention for vehicular networks due to its decentralized, transparent, and tamper-proof natures. However, the highly dynamic vehicular environment challenges the reliability of trust evaluation as well as the privacy preservation of vehicles against tracking attacks. In this paper, we propose a privacy-preserving trust management system to evaluate the trustworthiness of vehicles by exploiting the recent advanced blockchain techniques. Specifically, we build up a trust evaluation blockchain, where the trustworthiness of an involved vehicle is evaluated by distributed road-side units (RSUs) based on the rating feedback from neighboring vehicles. To enable efficient and privacy-preserving trust evaluation, we deploy the feedback messages aggregation and trust evaluation on two smart contracts, which are executed and verified by distributed RSUs automatically. In particular, identity authentication based on Elliptic Curve Cryptography (ECC) cryptosystem is introduced to prevent privacy leakage of vehicles. Security analysis and performance evaluation reveal that our system is secure and efficient to manage the trust evaluation while guaranteeing privacy-preservation for vehicular networks.
Qinnan Zhang, Qingyang Ding, Jianming Zhu, Dandan Li
Federated learning is a distributed machine learning framework that enables distributed model training with local datasets, which can effectively protect the data privacy of workers (i.e., intelligent edge nodes). The majority of federated learning algorithms assume that the workers are trusted and voluntarily participate in the cooperative model training process. However, the situation in practical application is not consistent with this. There are many challenges such as worker selection schemes for participating workers, which hamper the widespread adoption of federated learning. The existing research about worker selection scheme focused on multi-weight subjective logic model to calculate reputation value and adopted contract theory to motivate workers, which may exist subjective judgmental factors and unfair profit distribution. To address above challenges, we calculate the reputation value by model quality parameters to evaluate the reliability of workers. Blockchain is designed to store historical reputation value that realized tamperresistance and non-repudiation. Numerical results indicate that the worker selection scheme can improve the accuracy of the model and accelerate the model convergence.
The Internet of Vehicles (IoV) aims to perceive, compute, and process environmental data in a collaborative manner. Previous works focus on data sharing between vehicles, but a large amount of data will lead to redundant transmission and network congestion. In addition, security and privacy issues prevent these nodes from participating in the sharing process. Knowledge is extracted from data through machine learning (ML) and shared in the form of small-scale well-trained model parameters, which improves collaborative learning more effectively and relieves network pressure. While traditional ML algorithms are not suitable for distributed IoV with local characteristics. Based on this, this paper first divides the vehicles into multiple regions and proposes a Regional Federated Learning (RFL) framework, in which all regions maintain their own learning models, i.e. knowledge. We design a reputation mechanism to measure the reliability of vehicles participating in RFL. To address the security challenges brought by the untrusted centralized trading market, we propose a blockchain-enhanced knowledge trading framework, in which an authorized market agency coordinates the trading quickly. We model the optimal pricing mechanism as a non-cooperative game, taking into account the competition among all knowledge providers. Numerical simulation shows that the proposed reputation mechanism improves the accuracy of knowledge up to 18%, and the optimal knowledge pricing mechanism effectively increases the utility of market.
Despite the advantages of Federated Learning (FL), such as devolving model training to intelligent devices and preserving data privacy, FL still faces the risk of the single point of failure and attack from malicious participants. Recently, blockchain is considered a promising solution that can transform FL training into a decentralized manner and improve security during training. However, traditional consensus mechanisms and architecture for blockchain can hardly handle the large-scale FL task due to the huge resource consumption, limited throughput, and high communication complexity. To this end, this paper proposes a two-layer blockchain-driven FL framework, called as ChainsFL, which is composed of multiple Raft-based shard networks (layer-l) and a Direct Acyclic Graph (DAG)-based main chain (layer-2) where layer-l limits the scale of each shard for a small range of information exchange, and layer-2 allows each shard to update and share the model in parallel and asynchronously. Furthermore, FL procedure in a blockchain manner is designed, and the refined DAG consensus mechanism to mitigate the effect of stale models is proposed. In order to provide a proof-of-concept implementation and evaluation, the shard blockchain base on Hyperledger Fabric is deployed on the self-made gateway as layer-l, and the self-developed DAG-based main chain is deployed on the personal computer as layer-2. The experimental results show that ChainsFL provides acceptable and sometimes better training efficiency and stronger robustness comparing with the typical existing FL systems.
The barriers of food enterprises and departments caused information asymmetry, which is the root cause of food safety incidents. Simultaneously, it is challenging to solve the information asymmetry by the existing cloud-based food supply-chain regulation system. Establishing a secure and reliable data sharing environment is an effective solution to the information island. Blockchain can construct a security network based on mathematical algorithms, eliminating the third party’s potential security risk, and realize transparently share data. In this paper, on the principle of metadata remaining in the food enterprises, we propose a blockchain-cloud fusion scheme based on Decentralized Attribute-Based Signature (DABS) to realize secure data sharing between departments. It constructs a decentralized and trusting environment for data owners to share data and achieves social co-governance of food safety based on the smart contract. It can also preserve the existing system architecture and complement the performance disadvantage of blockchain and cloud storage. The result achieved from security analysis shows that our scheme supports unconditional full anonymity and can resist collusion attacks of N-1 out of N corrupted attribute authorities.
Sejong Lee, Jaehyeon Kim, Yongseok Kwon, Teasung Kim · 5 authors
BACKGROUND With the increasing sophistication of the medical industry, various advanced medical services such as medical artificial intelligence, telemedicine, and personalized health care services have emerged. The demand for medical data is also rapidly increasing today because advanced medical services use medical data such as user data and electronic medical records (EMRs) to provide services. As a result, health care institutions and medical practitioners are researching various mechanisms and tools to feed medical data into their systems seamlessly. However, medical data contain sensitive personal information of patients. Therefore, ensuring security while meeting the demand for medical data is a very important problem in the information age for which a solution is required. OBJECTIVE Our goal is to design a blockchain-based decentralized patient information exchange (PIE) system that can safely and efficiently share EMRs. The proposed system preserves patients’ privacy in the EMRs through a medical information exchange process that includes data encryption and access control. METHODS We propose a blockchain-based EMR-sharing system that allows patients to manage their EMRs scattered across multiple hospitals and share them with other users. Our PIE system protects the patient’s EMR from security threats such as counterfeiting and privacy attacks during data sharing. In addition, it provides scalability by using distributed data-sharing methods to quickly share an EMR, regardless of its size or type. We implemented simulation models using Hyperledger Fabric, an open source blockchain framework. RESULTS We performed a simulation of the EMR-sharing process and compared it with previous works on blockchain-based medical systems to check the proposed system’s performance. During the simulation, we found that it takes an average of 0.01014 (SD 0.0028) seconds to download 1 MB of EMR in our proposed PIE system. Moreover, it has been confirmed that data can be freely shared with other users regardless of the size or format of the data to be transmitted through the distributed data-sharing technique using the InterPlanetary File System. We conducted a security analysis to check whether the proposed security mechanism can effectively protect users of the EMR-sharing system from security threats such as data forgery or unauthorized access, and we found that the distributed ledger structure and re-encryption–based data encryption method can effectively protect users’ EMRs from forgery and privacy leak threats and provide data integrity. CONCLUSIONS Blockchain is a distributed ledger technology that provides data integrity to enable patient-centered health information exchange and access control. PIE systems integrate and manage fragmented patient EMRs through blockchain and protect users from security threats during the data exchange process among users. To increase safety and efficiency in the EMR-sharing process, we used access control using security levels, data encryption based on re-encryption, and a distributed data-sharing scheme.
This paper presents the use case of digitizing health certificates of prostitutes. It is shown that a centralized approach lacks trust in privacy by the prostitutes as well as not sufficient actions to ensure the integrity of digital health certificates. To counter these short comings, it is evaluated if a blockchain/distributed ledger technology approach should be considered. Based on this discussion, an evaluation of Ethereum Hyperledger Fabric and IOTA is presented, stating that IOTA matches the use case requirements the best. Therefore, an IOTA based architecture for digital health certificates of prostitutes is presented and discussed.
With the development of information and computer technology, internet of things (IoT) technology has brought tremendous changes to people's production and life. The emergence of IoT creates new opportunities to stimulate innovative services that better converge the information resources of IoT objects. However, due to the increasing scale of the network and the intelligence of devices, the development of the service-oriented IoT has also been accompanied by the issue of trust which is crucial to the security and stability of the system. Blockchain, as an emerging distributed ledger technology, is considered to be a driving force to improve the trust management of the service-oriented IoT. This research is a systematic analysis with the goal of illustrating and analyzing how the blockchain contributes to addressing the issue of trust management in service-oriented IoT. The objective of this paper is to understand the trust issue in the service-oriented IoT and analyze the potential blockchain-based solution, thereby motivating further research interest in this field.
After the acceptance of blockchain technology, there have been applications which aim to use blockchain in their fields. Various approaches have been proposed in past to build a secure Identity Management (IdM) System. This is a novel systematic literature mapping of IdM in blockchain. This paper provides an extensive review on IdM with emphasis on how the emergence of blockchain has addressed the IdM challenges faced over the years. A thorough study has been done on the existing literature. The primary and secondary “search string” were identified and search was conducted on five databases; and after screening the analysis was done. Out of the total studied literature, 30 primary studies published from 2009 to 2020 were selected. Through this paper, the researchers will be able to: 1) find out the research trends in IdM using blockchain, 2) understand the challenges in IdM and report whether blockchain can solve the IdM challenges, 3) scrutinize and understand how the different frameworks of IdM would deal with security, integrity and privacy problems, 4) know about initiatives taken for IdM using blockchain, 5) which consensus algorithms are popular among blockchains, 6) know about the research projects going on in the field of IdM using blockchain.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Evidence destruction and tempering is a time-tested tactic to protect the powerful perpetrators, criminals, and corrupt officials. Countries where law enforcing institutions and judicial system can be comprised, and evidence destroyed or tampered, ordinary citizens feel disengaged with the investigation or prosecution process, and in some instances, intimidated due to the vulnerability to exposure and retribution. Using Distributed Ledger Technologies (DLT), such as blockchain, as the underpinning technology, here we propose a conceptual model - 'EvidenceChain', through which citizens can anonymously upload digital evidence, having assurance that the integrity of the evidence will be preserved in an immutable and indestructible manner. Person uploading the evidence can anonymously share it with investigating authorities or openly with public, if coerced by the perpetrators or authorities. Transferring the ownership of evidence from authority to ordinary citizen, and custodianship of evidence from susceptible centralized repository to an immutable and indestructible distributed repository, can cause a paradigm shift of power that not only can minimize spoliation of evidence but human rights abuse too. Here the conceptual model was theoretically tested against some high-profile spoliation of evidence cases from four South Asian developing countries that often rank high in global corruption index and low in human rights index.
The security and privacy of healthcare enterprises (HEs) are crucial because they maintain sensitive information. Because of the unique functional requirement of omni-inclusiveness, HEs are expected to monitor patients, allowing for connectivity with vehicular ad hoc networks (VANETs). In the absence of literature on security provisioning frameworks that connect VANETs and HEs, this paper presents a smart zero-knowledge proof and statistical fingerprinting-based trusted secure communication framework for a fog computing environment. A zero-knowledge proof is used for vehicle authentication, and statistical fingerprinting is employed to secure communication between VANETs and HEs. Authenticity verification of the operations is performed at the on-board unit (OBU) fitted in the vehicle based on the service executions at the resident hardware platform. The processor clock cycles are acquired from the service executions in a complete sandboxed environment. The calculated cycles assist in developing the blueprint signature for the particular OBU of the vehicle. Hence, the fingerprint signature helps build trust and plays a key role in authenticating the vehicle's horizontal movement to everything or to different sections of the HEs. In an environment enabled for fog computing, our novel model can provide efficient remote monitoring.
With the development of autonomous driving and the Internet of Vehicles, vehicle data communication and data security become more and more important. Blockchain which has transparency, decentralization and immutability nature is treated as a promising approach to support intelligent vehicle systems. However, due to the high data update overhead, vulnerable raw data storage policy and inflexible consensus algorithm, traditional blockchain technologies are not suitable in modern vehicular systems. Hence, we propose, a blockchain data storage system that supports incremental data updating. Specifically, the system reduces the re-uploaded data size through smart contract and data partition to decrease the overhead. Besides, data replica and multi-data source addressing of index on the chain enhance the data reliability. In addition, an adaptive proof-of-work algorithm is developed, whose execution cost is dynamically adjusted based on nodes' behavior. It greatly improves the data record and updating efficiency. Comprehensive experimental results show that BUS can effectively improve the data updating efficiency with low overhead and fewer resources in intelligent vehicle scenarios.
Monik Raj Behera, sudhir upadhyay, Suresh Shetty, Robert Otter
In recent times, Machine learning and Artificial intelligence have become one of the key emerging fields of computer science. Many researchers and businesses are benefited by machine learning models that are trained by data processing at scale. However, machine learning, and particularly Deep Learning requires large amounts of data, that in several instances are proprietary and confidential to many businesses. In order to respect individual organization’s privacy in collaborative machine learning, federated learning could play a crucial role. Such implementations of privacy preserving federated learning find applicability in various ecosystems like finance, health care, legal, research and other fields that require preservation of privacy. However, many such implementations are driven by a centralized architecture in the network, where the aggregator node becomes the single point of failure, and is also expected with lots of computing resources at its disposal. In this paper, we propose an approach of implementing a decentralized, peer-topeer federated learning framework, that leverages RAFT based aggregator selection. The proposal hinges on that fact that there is no one permanent aggregator, but instead a transient, time based elected leader, which will aggregate the models from all the peers in the network. The leader ( aggregator) publishes the aggregated model on the network, for everyone to consume. Along with peer-to-peer network and RAFT based aggregator selection, the framework uses dynamic generation of cryptographic keys, to create a more secure mechanism for delivery of models within the network. The key rotation also ensures anonymity of the sender on the network too. Experiments conducted in the paper, verifies the usage of peer-to-peer network for creating a resilient federated learning network. Although the proposed solution uses an artificial neural network in it’s reference implementation, the generic design of the framework can accommodate any federated learning model within the network.
Security Credential Management System (SCMS) provides the Public Key Infrastructure (PKI) for vehicular networking. SCMS builds the state-of-the-art distributed PKI to protect the vehicular networking privacy against an honest-but-curious authority (by the use of multiple PKI authorities) and to decentralize the PKI root of trust (by the Elector-Based Root Management or EBRM, having the distributed electors manage the Root Certificate Authority or RCA). We build on the EBRM architecture and construct a Blockchain-Based Root Management (BBRM) to provide even greater decentralization and security. More specifically, BBRM uses blockchain to i) replace the existing RCA and have the electors directly involved in the root certificate generation, ii) control the elector network membership including elector addition and revocation, and iii) provide greater accountability and transparency on the aforementioned functionalities. We implement BBRM on Hyperledger Fabric using smart contract for system experimentation and analyses. Our experiments show that BBRM is lightweight in processing, efficient in ledger size, and supports a bandwidth of multiple transactions per second. Our results show that the BBRM blockchain is appropriate for the root certificate generation and the elector membership control for EBRM within SCMS, which are significantly smaller in number and occurrences than the SCMS outputs of vehicle certificates. We also experiment to analyze how the BBRM distributed consensus protocol parameters, such as the number of electors and the number of required votes, affect the overall scheme's performances.