Michael Sober, Giulia Scaffino, Stefan Schulte, Salil S. Kanhere
Abstract The (IoT) is growing steadily, and so is the number of data that is generated by (IoT) devices. This makes it difficult to find and leverage relevant data (and data sources) without a data marketplace. Such a marketplace provides a platform to enable different parties, e.g., sensor operators and service providers, to trade their data. Today, most data marketplaces are based on centralized solutions, which may become a single point of failure and come with expensive infrastructure, trust problems, and privacy issues. Therefore, we propose the application of blockchain technology to implement a data marketplace for the IoT. Within the proposed marketplace, smart contracts are used to implement various functionalities and enforce the rules of the data exchange. The marketplace also includes a proxy, a broker, and (GUIs) to enable data trading. To show the applicability of the proposed data marketplace, we analyze the costs arising from the utilization of smart contracts.
Anthony Simonet-Boulogne, Arnor Solberg, Amir Sinaeepourfard, Dumitru Roman · 8 authors
The rapid development of Smart Cities is aided by the convergence of information and communication technologies (ICT). Data is a key component of Smart City applications as well as a serious worry. Data is the critical factor that drives the whole development life-cycle in most Smart City use-cases, according to an exhaustive examination of several Smart City use-cases. Mishandling data, on the other hand, can have severe repercussions for programs that get incorrect data and users whose privacy may be compromised. As a result, we believe that an integrated ICT solution in Smart Cities is key to achieve the highest levels of scalability, data integrity, and secrecy within and across Smart Cities. As a result, this paper discusses a variety of modern technologies for Smart Cities and proposes our integrated architecture, which connects Blockchain technologies with modern data analytic techniques (e.g., Federated Learning) and Edge/Fog computing to address the current data privacy issues in Smart Cities. Finally, we discuss and present our proposed architectural framework in detail, taking into account an online marketing campaign and an e-Health application use-cases.
We reveal six zero-day social engineering attacks in Ethereum, and subdivide them into two classes: Address Manipulation and Homograph. We demonstrate the attacks by embedding them in source codes of five popular smart contracts with combined market capitalization of over \$29 billion, and show that the attacks have the ability to remain dormant during the testing phase and activate only after production deployment. We analyze 85,656 open source smart contracts and find 1,027 contracts that can be directly used for performing social engineering attacks. For responsible disclosure, we contact seven smart contract security firms. In the spirit of open research, we make the source codes of the attack benchmark, tools, and datasets available to the public.
Halima Mhamdi, Ben Othman Soufiene, Ahmed Zouinkhi, Obaid Ali · 5 authors
Blockchain technology is now regarded as one of the most interesting and possibly innovative technologies. It enables information to be stored and exchanged securely and transparently without the need for a centralized authority to regulate it. Some of the primary benefits of this technology are the atomicity of the stored data. Given its features, this technology has the potential to provide answers to challenges encountered in a very sensitive sector, namely, Internet of Vehicles (IoV). In IoV, vehicles and service providers autonomously capture and produce data without human intervention. This exchanged data must meet certain criteria such as decentralization, automation, security, and stakeholder trust management. To overcome these challenges, the integration of blockchain technology and multi-agent systems is a key solution. Based on smart contracts, the proposed solution consists of exploiting role-based access control (RBAC) and attribute-based access control (ABAC) techniques. This solution removes the central authority (CA) to reduce maintenance costs and eliminate legacy threats from centralized systems. The results, obtained from consumption costs, show that the developed platform is characterized by security, availability, and privacy.
Abdur Rehman, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal · 6 authors
In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the estimation of intrusion detection.
Yuling Chen, Junhong Tao, Tao Li, Jiangyuan Cai · 5 authors
Abstract Secure comparison protocol is an important branch of secure multi-party computation(SMPC), which compares the size of input data without disclosing any information between participants. The development of cloud computing provides an application platform for SMPC, but it also brings new challenges. In cloud computing with SMPC, clients need to process their own data and submit the processed data to a cloud server, which then performs the computation. In this process, not only the clients need to maintain an honest state at all times, but sensitive data on the cloud server side may also be exposed. In this paper, zero-knowledge proof and homomorphic encryption techniques are used to improve Damgård-Geisler-KrØigaard(DGK) comparison protocol. The improved secure comparison protocol can not only safely calculate private data, but also be applicable to malicious participant model. Finally, the security analysis shows that the proposed scheme not only ensures the privacy security of participants, but also ensures the data fairness of comparison protocols.
Attia Qammar, Ahmad Karim, Huansheng Ning, Jianguo Ding
Federated learning (FL) is a promising framework for distributed machine learning that trains models without sharing local data while protecting privacy. FL exploits the concept of collaborative learning and builds privacy-preserving models. Nevertheless, the integral features of FL are fraught with problems, such as the disclosure of private information, the unreliability of uploading model parameters to the server, the communication cost, etc. Blockchain, as a decentralized technology, is able to improve the performance of FL without requiring a centralized server and also solves the above problems. In this paper, a systematic literature review on the integration of Blockchain in federated learning was considered with the analysis of the existing FL problems that can be compensated. Through carefully screening, most relevant studies are included and research questions cover the potential security and privacy attacks in traditional federated learning that can be solved by blockchain as well as the characteristics of Blockchain-based FL. In addition, the latest Blockchain-based approaches to federated learning have been studied in-depth in terms of security and privacy, records and rewards, and verification and accountability. Furthermore, open issues related to the combination of Blockchain and FL are discussed. Finally, future research directions for the robust development of Blockchain-based FL systems are proposed.
Samuel Akwasi Frimpong, Han Mu, Edward Kwadwo Boahen, Rexford Nii Ayitey Sosu · 7 authors
Recommendation systems provide ease and convenience for users to address information overload problems while interacting with online platforms such as social media and e-commerce. However, it raises several questions about privacy, especially for users who prefer to remain anonymous, especially on online social networks (OSNs). Moreover, due to the commercialization of online users' data, some service providers sell users' data to third parties at the blind side of the users, which leads to trust issues between users and service providers. Such matters call for a system that gives online users much-needed control and autonomy of their data. With the advancement of blockchain technology, many research institutions are experimenting with decentralized technologies to resolve the OSN user dilemma of privacy intrusion against third parties and hacks. To resolve these limitations, we propose RecGuard, a privacy preservation blockchain-based network system. We developed two smart contracts, RG-SH and RG-ST, to ensure the security and privacy of user data. The RG-SH manages user data, whereas the RG-ST stores data. A graph convolutional network (GCN) was integrated with the blockchain-based system to detect malicious nodes. Finally, we implemented our framework prototype on a locally simulated network. The analysis and experiment results show that the proposed scheme demonstrates the effectiveness and privacy of users in our framework.
Arshad Ahmad Dar, Malik Zaib Alam, Adeel Ahmad, Faheem Ahmad Reegu · 5 authors
COVID-19 pandemic caused global epidemic infections, which is one of the most severe infections in human medical history. In the absence of proper medications and vaccines, handling the pandemic has been challenging for governments and major health facilities. Additionally, tracing COVID-19 cases and handling data generated from the pandemic are also extremely challenging. Data privacy access and collection are also a challenge when handling COVID-19 data. Blockchain technology provides various features such as decentralization, anonymity, cryptographic security, smart contracts, and a distributed framework that allows users and entities to handle COVID-19 data better. Since the outbreak has made the moral crisis in the clinical and administrative centers worse than any other that has resulted in the decline in the supply of the exact information, however, it is vital to provide fast and accurate insight into the situation. As a result of all these concerns, this study emphasizes the need for COVID-19 data processing to acquire aspects such as data security, data integrity, real-time data handling, and data management to provide patients with all benefits from which they had been denied owing to misinformation. Hence, the management of COVID-19 data through the use of the blockchain framework is crucial. Therefore, this paper illustrates how blockchain technology can be implemented in the COVID-19 data handling process. The paper also proposes a framework with three main layers: data collection layer; data access and privacy layer; and data storage layer.
Aiming at solving the challenges of the Internet of Vehicles (IoV), such as privacy sensitive data exposing, data being vulnerable to unauthorized access and tampered with and the single point of failure of the cloud server, this paper introduces a blockchain-enabled data access method based on attribute-based encryption. To enhance the privacy protection, the attributes are hidden and all generated transactions are recorded on the blockchain for auditing. Even if it is untrusted to a third party, it can guarantee the user’s control over his own data. To reduce the computation cost, decentralized edge nodes jointly maintain the blockchain and assist users with limited resources to complete most of the calculations. Meanwhile, the use of smart contracts not only reduces the verification workload, but also evaluates multiple factors such as the communication range, communication time, and calculation overhead of the roadside unit (RSU) to ensure that the vehicle can receive messages in a timely manner. In addition, timely updating the attributes can reduce the user’s computational burden and increase the flexibility of the system. Numeral experiments to show the proposed scheme has many advantages of over others such as the storage cost, the whole time consume of the proposed algorithms and the proxy computation cost.
Jofina Jijin, Boon‐Chong Seet, Peter Han Joo Chong
The opportunistic fog radio access network (OF-RAN) expands its offloading computation capacity on-demand by establishing virtual fog access points (v-FAPs), comprising user devices with idle resources recruited opportunistically to execute the offloaded tasks in a distributed manner. OF-RAN is attractive for providing computation offloading services to resource-limited Internet-of-Things (IoT) devices from vertical industrial applications such as smart transportation, tourism, mobile healthcare, and public safety. However, the current OF-RAN design is lacking a trusted and distributed mechanism for automating its processes such as v-FAP formation and service execution. Motivated by the recent emergence of blockchain, with smart contracts as an enabler of trusted and distributed systems, we propose an automated mechanism for OF-RAN processes using smart contracts. To demonstrate how our smart-contract-based automation for OF-RAN could apply in real life, a federated deep learning (DL) use-case where a resource-limited client offloads the resource-intensive training of its DL model to a v-FAP is implemented and evaluated. The results validate the DL and blockchain performances of the proposed smart-contract-enabled OF-RAN. The appropriate setting of process parameters to meet the often competing requirements is also demonstrated.
Machine learning (ML) has penetrated various fields in the era of big data. The advantage of collaborative machine learning (CML) over most conventional ML lies in the joint effort of decentralized nodes or agents that results in better model performance and generalization. As the training of ML models requires a massive amount of good quality data, it is necessary to eliminate concerns about data privacy and ensure high-quality data. To solve this problem, we cast our eyes on the integration of CML and smart contracts. Based on blockchain, smart contracts enable automatic execution of data preserving and validation, as well as the continuity of CML model training. In our simulation experiments, we define incentive mechanisms on the smart contract, investigate the important factors such as the number of features in the dataset (num_words), the size of the training data, the cost for the data holders to submit data, etc., and conclude how these factors impact the performance metrics of the model: the accuracy of the trained model, the gap between the accuracies of the model before and after simulation, and the time to use up the balance of bad agent. For instance, the increase of the value of num_words leads to higher model accuracy and eliminates the negative influence of malicious agents in a shorter time from our observation of the experiment results. Statistical analyses show that with the help of smart contracts, the influence of invalid data is efficiently diminished and model robustness is maintained. We also discuss the gap in existing research and put forward possible future directions for further works.
The field of digital pathology produces a large number of images associated with patient metadata that are the raw material of computational pathology. The process of making images available with adequate privacy and data protection considerations takes a long time. Given that the Ethereum network associated with InterPlanetary File System (IPFS) promotes the exchange of information in a secure, private and decentralized manner, this association could be an important partner between digital and computational pathology. Therefore, here we propose and discuss a prototype with the aforementioned parts and the addition of neural compression, as an essential information preservation step. This prototype could constitute a link for the exchange of information in a secure way, providing transparency and reliability to the chain and empowering the field of manufacturing artificial vision solutions for the medical field
Abdullah Alghamdi, Jiang Zhu, Guocai Yin, Mohammad Shorfuzzaman · 7 authors
Resource constraint Consumer Internet of Things (CIoT) is controlled through gateway devices (e.g., smartphones, computers, etc.) that are connected to Mobile Edge Computing (MEC) servers or cloud regulated by a third party. Recently Machine Learning (ML) has been widely used in automation, consumer behavior analysis, device quality upgradation, etc. Typical ML predicts by analyzing customers' raw data in a centralized system which raises the security and privacy issues such as data leakage, privacy violation, single point of failure, etc. To overcome the problems, Federated Learning (FL) developed an initial solution to ensure services without sharing personal data. In FL, a centralized aggregator collaborates and makes an average for a global model used for the next round of training. However, the centralized aggregator raised the same issues, such as a single point of control leaking the updated model and interrupting the entire process. Additionally, research claims data can be retrieved from model parameters. Beyond that, since the Gateway (GW) device has full access to the raw data, it can also threaten the entire ecosystem. This research contributes a blockchain-controlled, edge intelligence federated learning framework for a distributed learning platform for CIoT. The federated learning platform allows collaborative learning with users' shared data, and the blockchain network replaces the centralized aggregator and ensures secure participation of gateway devices in the ecosystem. Furthermore, blockchain is trustless, immutable, and anonymous, encouraging CIoT end users to participate. We evaluated the framework and federated learning outcomes using the well-known Stanford Cars dataset. Experimental results prove the effectiveness of the proposed framework.
The cloud-aided sharing of e-healthcare data has great positive significance for research. However, due to the privacy consideration, these data are usually encrypted before uploading to the cloud server which impedes data sharing between different medical institutions. Conditional proxy re-encryption (CPRE) allows the proxy to converse ciphertext, especially by specifying a condition embed in the re-encryption key to achieve fine-grained access control over the ciphertext. Unfortunately, existing CPRE schemes cannot ensure the privacy of the condition, which may contain some sensitive private information. Furthermore, a malicious proxy server may return part of the results and even false data to save its computation or bandwidth. To solve these problems, we propose a blockchain-based condition invisible proxy re-encryption scheme for the e-healthcare system. The proposed scheme guarantees the confidentiality of the data by hiding the condition in the re-encryption key so that the proxy cannot learn anything about the condition. Moreover, the ciphertext-searching algorithm is leveraged by executing the smart contract in the blockchain which ensures the results are correct and complete. Finally, experiment results demonstrate the practicability of the proposed scheme in applications.
The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering significant advantages in agility, responsiveness, and potential environmental benefits. The number and variety of IoT devices are sharply increasing, and as they do, they generate significant data sources. Deep learning (DL) algorithms are increasingly integrated into IoT applications to learn and infer patterns and make intelligent decisions. However, current IoT paradigms rely on centralized storage and computing to operate the DL algorithms. This key central component can potentially cause issues in scalability, security threats, and privacy breaches. Federated learning (FL) has emerged as a new paradigm for DL algorithms to preserve data privacy. Although FL helps reduce privacy leakage by avoiding transferring client data, it still has many challenges related to models’ vulnerabilities and attacks. With the emergence of blockchain and smart contracts, the utilization of these technologies has the potential to safeguard FL across IoT ecosystems. This study aims to review blockchain-based FL methods for securing IoT systems holistically. It presents the current state of research in blockchain, how it can be applied to FL approaches, current IoT security issues, and responses to outline the need to use emerging approaches toward the security and privacy of IoT ecosystems. It also focuses on IoT data analytics from a security perspective and the open research questions. It also provides a thorough literature review of blockchain-based FL approaches for IoT applications. Finally, the challenges and risks associated with integrating blockchain and FL in IoT are discussed to be considered in future works.
As a part of the intelligent transportation system, vehicular ad hoc networks (VANETs) provide timely information about road events and traffic to improve road safety and traffic efficiency. However, VANETs face many challenges, such as attacks from malicious vehicles, identity privacy leakage, and the absence of trust between vehicular nodes. In addition, vehicles nearby an event usually lack the motivation to participate in the traffic event validation whenever it occurs, which requires the cooperation of vehicles on the network. To solve these problems, a blockchain-enabled incentive trust model with a privacy-preserving threshold ring signature scheme for VANETs is proposed. Firstly, a threshold ring signature scheme is designed in order to allow participants in the non-trusted environment to anonymously witness the message's authenticity and reliability while guaranteeing the vehicle's privacy. Second, a blockchain-enabled incentive trust management model is presented to enable the roadside units (RSUs) to thwart various attacks and guarantee the trustworthiness of event messages transmitted in VANETs and also motivate the senders of the traffic information and their witnesses with incentives. Finally, to improve efficiency, a practical Byzantine fault-tolerant consensus mechanism is used. Our proposed system is demonstrated to be effective and secure for VANETs, according to both security analysis and performance evaluation.
Many regions are in urgent need of facial masks for slowing down the spread of COVID-19. To fight the pandemic, people are contributing masks through donation systems. Most existing systems are built on a centralized architecture which is prone to the single point of failure and lack of transparency. Blockchain-based solutions neglect fundamental privacy concerns (donation privacy) and security attacks (collusion attack, stealing attack). Moreover, current auditing solutions are not designed to achieve donation privacy, thus not appropriate in our context. In this work, we design a decentralized, anonymous, and secure auditing frameworkAstraeabased on private smart contracts for donation systems. Specifically, we integrate a Distribute Smart Contract (DiSC) with an SGX Enclave to distribute donations, prove the integrity of donation number (intention) and donation sum while preserving donation privacy. With DiSC, we design a Donation Smart Contract to refund deposits and defend against the stealing attack the collusion attack from malicious collector and transponder. We formally define and prove the privacy and security of Astraea by using security reduction. We build a prototype of Astraea to conduct extensive performance analysis. Experimental results demonstrate that Astraea is practically efficient in terms of both computation and communication.
Mateusz Godyn, Michał Kędziora, Yingying Ren, Yongxin Liu · 5 authors
The aim of this paper was to perform an analysis of the state-of-the-art solutions of the permissioned blockchain compliance with the General Data Protection Regulation (GDPR), including the implementation of one of the analyzed methods and the own solution. This paper covers the subject of GDPR and its impact on already existing blockchain databases to determine the domain of the problem, including the necessity to introduce mutability in the data structure to comply with the "right to be forgotten". The performed analysis made it possible to discuss current research in technical terms as well as in the regulation itself. In the experimental part, attempts were made to research and implement the Reference-based Tree Structure (RBTS), including the performance tests. The proposed solution is efficient and easily reproducible. The deletion of unwanted content is quick and requires consent only from the owner of personal data; therefore, eliminating the dependency on the other blockchain network participants.
Xin Liu Xin Liu, Yang Xu Xin Liu, Gang Xu Yang Xu, Xiu-Bo Chen Gang Xu · 5 authors
<p>With the rapid development of the Internet and information technology, the problem of zero-trust networks has become increasingly prominent, and secure multi-party computation has become a research hotspot to solve the problem of zero-trust networks. The secure judgment of point and line relationship is an important research branch of secure computing set geometry. However, most of resent secure computing protocols of point and line relationship are designed in the semi-honest model and cannot resist malicious attacks. Therefore, this paper analyzes the possible malicious adversary behaviors and designs a secure protocol in the malicious model. In this paper, the Paillier cryptosystem, zero- knowledge proof, and cut-choose method are used to resist malicious behavior, and the real/ideal model paradigm method is used to prove the security of the protocol. Compared with the existing solutions, the malicious model protocol is still efficient and widely used in real applications.</p> <p>&nbsp;</p>
Yanan Gong, K. P. Chow, Siu Ming Yiu, Hing Fung Ting
Bitcoin is a popular and widely traded cryptocurrency. The Bitcoin blockchain technology makes it easy for users to conduct pseudo-anonymous financial transactions. However, it also facilitates criminals to secrete their actual identities from law enforcement agencies. Heuristic-based address clustering is the subject regarding Bitcoin de-anonymization. But no heuristic algorithm has a known or potential error rate due to the lack of ground truth. This paper uses sensitivity analysis to validate and verify a constructed Bitcoin simulation model. The evaluation and validation processes examine the model behavior and model outputs from multiple simulation runs to demonstrate fidelity and credibility. The analysis results show no model uncertainties, and the simulation model is stable and can effectively simulate Bitcoin transactions. With a reasonable number of nodes and transaction volumes in the simulated network, the simulation model can be used to verify the effectiveness of two widely used heuristic-based address clustering algorithms and measure the corresponding error rates.
As IoT becomes omnipresent vast amounts of data are generated, which can be used for building innovative applications. However,interoperability issues and security concerns, prevent harvesting the full potentials of these data. In this paper we consider the use case of data generated by smart buildings. Buildings are becoming ever "smarter" by integrating IoT devices that improve comfort through sensing and automation. However, these devices and their data are usually siloed in specific applications or manufacturers, even though they can be valuable for various interested stakeholders who provide different types of "over the top" services, e.g., energy management. Most data sharing techniques follow an "all or nothing" approach, creating significant security and privacy threats, when even partially revealed, privacy-preserving, data subsets can fuel innovative applications. With these in mind we develop a platform that enables controlled, privacy-preserving sharing of data items. Our system innovates in two directions: Firstly, it provides a framework for allowing discovery and selective disclosure of IoT data without violating their integrity. Secondly, it provides a user-friendly, intuitive mechanisms allowing efficient, fine-grained access control over the shared data. Our solution leverages recent advances in the areas of Self-Sovereign Identities, Verifiable Credentials, and Zero-Knowledge Proofs, and it integrates them in a platform that combines the industry-standard authorization framework OAuth 2.0 and the Web of Things specifications.
Traditional ways of distributing and verifying academic certificates are not efficient. Certificates are distributed as hard copy. Verifying the integrity of the certificate is a time and resource consuming process. As a result, forged certificates have become common. It is very difficult to differentiate between a real and a forged certificate. Through our system, we intend to make the certificate generation, distribution, and verification process seamless. Any student can enter his or her personal details, academic coursework details, and the university code, and thus submit a certificate request to the university. University admins can verify the certificate requests, and approve or reject the requests as per their policy. Any student or third party could verify the integrity of the certificate by entering the details of the certificate under scrutiny into the system.