Abstract The Robot Operating System (ROS) streamlines human processes, increasing the efficiency of various production tasks. However, the security of data transfer operations in ROS is still in its immaturity. Securing data exchange between several robots is a significant problem. This paper proposes \textit{AuthROS}, an Ethereum blockchain-based secure data sharing method, for robot communication. It is a ROS node authorization system capable of ensuring the immutability and security of private data flow between ROS nodes of any size. To ensure data security, AuthROS employs the smart contract for permission granting and identification, SM2-based key exchange, and SM4-based plaintext encryption techniques. In addition, we deploy a data digest upload technique to optimize data query and upload performance. Finally, the experimental findings reveal that AuthROS has strong security, time performance, and node forging in cases where data should be recorded and robots need to remain immobile.
Stavros Salonikias, Marie Khair, ĪĪµĻĪ“ĻĻĪæĻ ĪĪ¬ĻĻĪæĻĪ±Ļ, Ioannis Mavridis
The COVID-19 pandemic further outlined the importance of global healthcare services provisioning for diagnosing and treating patients who tend to travel and live for large periods away from home and can be anywhere at any given time. Advances in technology enable healthcare practitioners to access critical data regarding a personās health status to provide better services. Medical data are sensitive in nature, and therefore, a reliable mechanism should ensure that only authorized entities can access data when needed. This paper, through a layered consideration of a Globalized Healthcare Provisioning Ecosystem (GHPE), reveals the interdependencies among its major components and suggests a necessary abstraction to identify requirements for the design of an access control suitable for the ecosystem. These requirements are imposed by the nature of the medical data as well as by the newly introduced potentials of Internet of Medical Things (IoMT) devices. As a result, an attribute-based access control framework is proposed aiming to provide prompt and secure access to medical data globally by utilizing state-of-the-art technologies and standards, including Next-Generation Access Control (NGAC), blockchain and smart contracts. Three types of smart contracts are proposed that enable access control to implement attribute and policy stores where policy classes and attributes are decentralized and immutable. In addition, the usage of blockchain-based distributed identities allows patients to be in control of access to their medical data and also enables healthcare service providers to access medical data promptly and reliably through the proposed access control framework. The qualitative characteristics of the proposed approach toward a decentralized and patient-centric access control in GHPE are demonstrated and discussed based on an application paradigm.
Due to the high transmission rate and high pathogenicity of the novel coronavirus (COVID-19), there is an urgent need for the diagnosis and treatment of outbreaks around the world. In order to diagnose quickly and accurately, an auxiliary diagnosis method is proposed for COVID-19 based on federated learning and blockchain, which can quickly and effectively enable collaborative model training among multiple medical institutions. It is beneficial to address data sharing difficulties and issues of privacy and security. This research mainly includes the following sectors: in order to address insufficient medical data and the data silos, this paper applies federated learning to COVID-19's medical diagnosis to achieve the transformation and refinement of big data values. With regard to third-party dependence, blockchain technology is introduced to protect sensitive information and safeguard the data rights of medical institutions. To ensure the model's validity and applicability, this paper simulates realistic situations based on a real COVID-19 dataset and analyses problems such as model iteration delays. Experimental results demonstrate that this method achieves a multiparty participation in training and a better data protection and would help medical personnel diagnose coronavirus disease more effectively.
Open access
Privacy-Preserving Technologies in Data
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Yuntao Wang, Haixia Peng, Zhou Su, Tom H. Luan Ā· 6 authors
Proof of work (PoW), as the representative consensus protocol for blockchain, consumes enormous amounts of computation and energy to determine bookkeeping rights among miners but does not achieve any practical purposes. To address the drawback of PoW, we propose a novel energy-recycling consensus mechanism named platform-free proof of federated learning (PF-PoFL), which leverages the computing power originally wasted in solving hard but meaningless PoW puzzles to conduct practical federated learning (FL) tasks. Nevertheless, potential security threats and efficiency concerns may occur due to the untrusted environment and minersā self-interested features. In this paper, by devising a novel block structure, new transaction types, and credit-based incentives, PF-PoFL allows efficient artificial intelligence (AI) task outsourcing, federated mining, model evaluation, and reward distribution in a fully decentralized manner, while resisting spoofing and Sybil attacks. Besides, PF-PoFL equips with a user-level differential privacy mechanism for miners to prevent implicit privacy leakage in training FL models. Furthermore, by considering dynamic miner characteristics (e.g., training samples, non-IID degree, and network delay) under diverse FL tasks, a federation formation game-based mechanism is presented to distributively form the optimized disjoint miner partition structure with Nash-stable convergence. Extensive simulations validate the efficiency and effectiveness of PF-PoFL.
Houpeng Hu, Jiaxiang Ou, Bin Qian, Yi Luo Ā· 7 authors
E-voting allows us to build a democratic business in most Internet of things (IoT) systems. For example, we may vote to choose a proper energy broker in a smart grid system. In this study, we focus on e-voting services in an Internet of energy (IoE) system, which is a new-style smart grid. A practical e-voting in IoE may focus on the properties of fairness, decentralization, eligibility, anonymity, compatibility, verifiability, and coercion resistance. It is difficult to fulfil all these properties simultaneously. Traditional voting schemes often use a public bulletin board or administrator in the voting process, which makes them become centralized. Services that offer e-voting via blockchain can make the voting schemes decentralized. However, many of them ignore the complexity of organizing the data of the transactions, which should be confirmed by the miners. Moreover, to the best of the authorsā knowledge, no works have tested the performance in the blockchain while considering practical use cases and constraints. Concerning all the challenges, we propose a practical anonymous voting scheme for IoE called IoEPAV. The proposed scheme fulfils all the mentioned design goals simultaneously. We tested IoEPAV both in different test networks of the Ethereum blockchain to give an overall evaluation. The practical evaluation can show that the proposed scheme is easy to be integrated into a real system like IoE. We also gave a comparison analysis with the state-of-the-art blockchain-based e-voting. All the results show that IoEPAV is decentralized, verifiable, anonymous, and highly efficient.
False messages sent by malicious or selfish vehicle nodes will reduce the operation efficiency of the Internet of Vehicles, and can even endanger drivers in serious cases. Therefore, it is very important to detect malicious vehicle nodes in the network in a timely manner. At present, the existing research on detecting malicious vehicle nodes in the Internet of Vehicles has some problems, such as difficulties with identification and a low detection efficiency. Blockchain technology cannot be tampered with or deleted and has open and transparent characteristics. Therefore, as a shared distributed ledger in decentralized networking, blockchain can promote collaboration between transactions, processing and interaction equipment, and help to establish a scalable, universal, private, secure and reliable car networking system. This paper puts forward a block-network-based malicious node detection mechanism. Using blockchain technology in a car network for malicious node identification algorithm could create a security scheme that can ensure smooth communication between network vehicles. A consensus on legal vehicle identification, message integrity verification, false message identification and malicious vehicle node identification form the four parts of the security scheme. Based on the publicāprivate key mechanism and RSA encryption algorithm, combined with the malicious node identification algorithm in the Internet of Vehicles, the authenticity of the vehicleās identity and message is determined to protect the vehicleās security and privacy. First, a blockchain-based, malicious node detection architecture is constructed for the Internet of vehicles. We propose a malicious node identification algorithm based on the blockchain consensus mechanism. Combined the above detection architecture with the consensus mechanism, a comprehensive and accurate verification of vehicle identity and message authenticity is ensured, looking at the four aspects of vehicle identification, accounting node selection, verification of transmission message integrity and identification of the authenticity of transmission messages. Subsequently, the verification results will be globally broadcast in the Internet of Vehicles to suppress malicious behavior, further ensure that reliable event messages are provided for the driver, improve the VANET operation environment, and improve the operation efficiency of the Internet of Vehicles. Comparing the proposed detection mechanism using simulation software, the simulation results show that the proposed blockchain-based trust detection mechanism can effectively improve the accuracy of vehicle node authentication and identification of false messages, and improve network transmission performance in the Internet of Vehicles environment.
Yinghui Zhang, Xuanni Wei, Jin Cao, Jianting Ning Ā· 6 authors
With the rapid development of edge computing technologies, smart healthcare significantly improves peopleās lives by collecting and analyzing health data in real time. However, security and privacy issues impede the wide deployment of smart healthcare systems (SHS). Most of existing solutions still have drawbacks with respect to computation efficiency and usersā privacy. In this paper, a blockchain-enabled attribute-based access control scheme with hidden policies is proposed for SHS. The scheme introduces multiple authorities to avoid single point failure. Especially, the mode of online-offline encryption relieves usersā online computation burden by transferring computation tasks to the idle time of users, and policy hiding protects usersā sensitive information. Furthermore, fair payments are realized based on blockchain and smart contracts to support the outsourcing of decryption between users and mobile edge computing servers. Finally, the proposed scheme is proven secure in the random oracle model, and experimental results show that it is computationally efficient and hence can be used in the edge computing environment.
Mirko Zichichi, Stefano Ferretti, Victor RodrıĢguez-Doncel
Big Tech companies operating in a data-driven economy offer services that rely on their users' personal data and usually store this personal information in "data silos" that prevent transparency about their use and opportunities for data sharing for public interest. In this paper, we present a solution that promotes the development of decentralized personal data marketplaces, exploiting the use of Distributed Ledger Technologies (DLTs), Decentralized File Storages (DFS) and smart contracts for storing personal data and managing access control in a decentralized way. Moreover, we focus on the issue of a lack of efficient decentralized mechanisms in DLTs and DFSs for querying a certain type of data. For this reason, we propose the use of a hypercube-structured Distributed Hash Table (DHT) on top of DLTs, organized for efficient processing of multiple keyword-based queries on the ledger data. We test our approach with the implementation of a use case regarding the creation of citizen-generated data based on direct participation and the involvement of a Decentralized Autonomous Organization (DAO). The performance evaluation demonstrates the viability of our approach for decentralized data searches, distributed authorization mechanisms and smart contract exploitation.
Abstract With the increasing utilization of space related data, the demand for spatial big data sharing and trading is growing rapidly, which promotes the emergence of spatial data market. However, in conventional data markets, both data buyers and data sellers have to use a centralized trading platform which might be dishonest. Blockchain is a decentralized distributed data storage technology, which uses the traceability and unforgeability to confirm and record each transaction, and can solve partial disadvantages of the centralized data market; unfortunately, it also introduces the problems of security and privacy. To address this issue, in this paper, we propose a blockchain-based spatial data trading framework with trusted execution environment to provide a trusted decentralized platform, including data storage, data query, data pricing, data reputation and security computing. Based on this framework, we use an auction pricing mechanism to ensure data trading authenticity and efficiency. At last, a spatial data trading framework was implemented and its effectiveness and security were verified.
Differential Privacy (DP) is often presented as a strong privacy-enhancing technology with broad applicability and advocated as a de facto standard for releasing aggregate statistics on sensitive data. However, in many embodiments, DP introduces a new attack surface: a malicious entity entrusted with releasing statistics could manipulate the results and use the randomness of DP as a convenient smokescreen to mask its nefariousness. Since revealing the random noise would obviate the purpose of introducing it, the miscreant may have a perfect alibi. To close this loophole, we introduce the idea of Interactive Proofs For Differential Privacy, which requires the publishing entity to output a zero knowledge proof that convinces an efficient verifier that the output is both DP and reliable. Such a definition might seem unachievable, as a verifier must validate that DP randomness was generated faithfully without learning anything about the randomness itself. We resolve this paradox by carefully mixing private and public randomness to compute verifiable DP counting queries with theoretical guarantees and show that it is also practical for real-world deployment. We also demonstrate that computational assumptions are necessary by showing a separation between information-theoretic DP and computational DP under our definition of verifiability.
The number of internet-connected devices has been exponentially growing with the massive volume of heterogeneous data generated from various devices, resulting in a highly intertwined cyber-physical system. Currently, the Edge Intelligence System (EIS) concept that leverages the merits of edge computing and Artificial Intelligence (AI) is utilized to provide smart cloud services with powerful computational processing and reduce decision-making delays. Thus, EIS offers a possible solution to realizing future Intelligent Transportation Systems (ITS), especially in a vehicular network framework. However, since the central aggregator server supervises the entire system orchestration, the existing EIS framework faces several challenges and is still potentially susceptible to numerous malicious attacks. Hence, to solve the issues mentioned earlier, this paper presents the notion of secure edge intelligence, merging the benefits of Federated Learning (FL), blockchain, and Local Differential Privacy (LDP). The blockchain-assisted FL approach efficiently improves traffic prediction accuracy and enhances user privacy and security by recording transactions in immutable distributed ledger networks and providing a decentralized reward mechanism system. Furthermore, LDP is empowered to strengthen the confidentiality of data sharing transactions, especially in protecting users' private data from various attacks. The proposed framework has been implemented in two scenarios, i.e., blockchain-based FL to efficiently develop the decentralized traffic management for vehicular networks and LDP-based FL to produce randomized privacy protection using the IBM Library for differential privacy.
Movsowitz Davidow, Danielle, Manevich, Yacov, Toch, Eran
Differential Privacy (DP) is often presented as a strong privacy-enhancing technology with broad applicability and advocated as a de-facto standard for releasing aggregate statistics on sensitive data. However, in many embodiments, DP introduces a new attack surface: a malicious entity entrusted with releasing statistics could manipulate the results and use the randomness of DP as a convenient smokescreen to mask its nefariousness. Since revealing the random noise would obviate the purpose of introducing it, the miscreant may have a perfect alibi. To close this loophole, we introduce the idea of \textit{Verifiable Differential Privacy}, which requires the publishing entity to output a zero-knowledge proof that convinces an efficient verifier that the output is both DP and reliable. Such a definition might seem unachievable, as a verifier must validate that DP randomness was generated faithfully without learning anything about the randomness itself. We resolve this paradox by carefully mixing private and public randomness to compute verifiable DP counting queries with theoretical guarantees and show that it is also practical for real-world deployment. We also demonstrate that computational assumptions are necessary by showing a separation between information-theoretic DP and computational DP under our definition of verifiability.
In recent years, decentralized applications such as Distributed Ledger Technologies and blockchain have evolved as suitable applications for secure sharing of information in a decentralized fashion using privacy preserving techniques like zero-knowledge protocols. However, the biggest issue with the traditional zero-knowledge protocols on a blockchain ledger is their slow performance on big data. This paper presents the advance zero-knowledge ledger by replacing their range-proof technique with the most efficient range-proof technique based on the improved inner product based zero-knowledge proofs. Moreover, this technique allows the aggregation of multiple range-proofs into a single range-proof, which makes the current zero-knowledge ledger system more efficient than the existing one.
Khizar Hameed, Ali Raza, Saurabh Garg, Muhammad Bilal Amin
An authorisation has been recognised as an important security measure for preventing unauthorised access to critical resources, such as devices and data, within the Internet of Things (IoT) networks. Existing authorisation methods for the IoT network are based on traditional access control models, which have several drawbacks, including architecture centralisation, policy tampering, access rights validation, malicious third-party policy assignment and control, and network-related overheads. The increasing trend of integrating Blockchain technology with IoT networks demonstrates its importance and potential to address the shortcomings of traditional IoT network authorisation mechanisms. This paper proposes a decentralised, secure, dynamic, and flexible authorisation scheme for IoT networks based on attribute-based access control (ABAC) fine-grained policies stored on a distributed immutable ledger. We design a Blockchain-based ABAC policy management framework divided into Attribute Management Authority (AMA) and Policy Management Authority (PMA) frameworks that use smart contract features to initialise, store, and manage attributes and policies on the Blockchain. To achieve flexibility and dynamicity in the authorisation process, we capture and utilise the environmental-related attributes in conjunction with the subject and object attributes of the ABAC model to define the policies. Furthermore, we designed the Blockchain-based Access Management Framework (AMF) to manage user requests to access IoT devices while maintaining the privacy and auditability of user requests and assigned policies. We implemented a prototype of our proposed scheme and executed it on the local Ethereum Blockchain. Finally, we demonstrated the applicability and flexibility of our proposed scheme for an IoT-based smart home scenario, taking into account deployment, execution and financial costs.
The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable decisions. As a nascent branch for trustworthy AI, federated learning (FL) has been regarded as a promising privacy preserving framework for training a global AI model over collaborative devices. However, security challenges still exist in the FL framework, e.g., Byzantine attacks from malicious devices, and model tampering attacks from malicious server, which will degrade or destroy the accuracy of trained global AI model. In this paper, we shall propose a decentralized blockchain based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server. However, to implement B-FL system at the network edge, multiple rounds of cross-validation in blockchain consensus protocol will induce long training latency. We thus formulate a network optimization problem that jointly considers bandwidth and power allocation for the minimization of long-term average training latency consisting of progressive learning rounds. We further propose to transform the network optimization problem as a Markov decision process and leverage the deep reinforcement learning based algorithm to provide high system performance with low computational complexity. Simulation results demonstrate that B-FL can resist malicious attacks from edge devices and servers, and the training latency of B-FL can be significantly reduced by deep reinforcement learning based algorithm compared with baseline algorithms.
Searchable encryption enables users to enjoy search services while protecting the security and privacy of their outsourced data. Blockchain-enabled searchable encryption delivers the computing processes that are executed on the server to the decentralized and transparent blockchain system, which eliminates the potential threat of malicious servers invading data. Recently, although some of the blockchain-enabled searchable encryption schemes realized that users can search freely and verify search results, unfortunately, these schemes were inefficient and costly. Motivated by this, we proposed an improved scheme that supports fine-grained access control and flexible searchable encryption. In our framework, the data owner uploads ciphertext documents and symmetric keys to cloud database and optional KMS, respectively, and manipulates the access control process and searchable encryption process through smart contracts. Finally, the experimental comparison conducted on a private Ethereum network proved the superiority of our scheme.
Recently, blockchain-based federated learning (BFL) has attracted intensive research attention due to that the training process is auditable and the architecture is serverless avoiding the single point failure of the parameter server in vanilla federated learning (VFL). Nevertheless, BFL tremendously escalates the communication traffic volume because all local model updates (i.e., changes of model parameters) obtained by BFL clients will be transmitted to all miners for verification and to all clients for aggregation. In contrast, the parameter server and clients in VFL only retain aggregated model updates. Consequently, the huge communication traffic in BFL will inevitably impair the training efficiency and hinder the deployment of BFL in reality. To improve the practicality of BFL, we are among the first to propose a fast blockchain-based communication-efficient federated learning framework by compressing communications in BFL, called BCFL. Meanwhile, we derive the convergence rate of BCFL with non-convex loss. To maximize the final model accuracy, we further formulate the problem to minimize the training loss of the convergence rate subject to a limited training time with respect to the compression rate and the block generation rate, which is a bi-convex optimization problem and can be efficiently solved. To the end, to demonstrate the efficiency of BCFL, we carry out extensive experiments with standard CIFAR-10 and FEMNIST datasets. Our experimental results not only verify the correctness of our analysis, but also manifest that BCFL can remarkably reduce the communication traffic by 95-98% or shorten the training time by 90-95% compared with BFL.
Mirko Zichichi, Stefano Ferretti, Gabriele DāAngelo, Victor RodrıĢguez-Doncel
Abstract The centralization of control over the processing of personal data threatens the privacy of individuals due to the lack of transparency and the obstruction of easy access to their data. Individuals need the tools to effectively exercise their rights, enshrined in regulations such as the European Union General Data Protection Regulation (GDPR). Having direct control over the flow of their personal data would not only favor their privacy but also a ādata altruismā, as supported by the new European proposal for a Data Governance Act. In this work, we propose a multi-layered architecture for the management of personal information based on the use of distributed ledger technologies (DLTs). After an in-depth analysis of the tensions between the GDPR and DLTs, we propose the following components: (1) a personal data storage based on a (possibly decentralized) file storage (DFS) to guarantee data sovereignty to individuals, confidentiality and data portability; (2) a DLT-based authorization system to control access to data through two distributed mechanisms, i.e. secret sharing (SS) and threshold proxy re-encryption (TPRE); (3) an audit system based on a second DLT. Furthermore, we provide a prototype implementation built upon an Ethereum private blockchain, InterPlanetary File System (IPFS) and Sia and we evaluate its performance in terms of response time.
G. Yedukondalu, Channapragada Rama Seshagiri Rao, Raman Dugyala
Industrial Internet of Things (IIoT) is changing many driving enterprises like transportation, mining, horticulture, energy and medical care. Machine Learning calculations are utilized for getting stages for IT frameworks. The IoT network unit hubs typically asset in a strange manner by making them more responsible to digital assaults. IIoT frameworks requests various situations in genuine one among them is giving security and the causes that encompass them in true viewpoints. It incorporates a system called PriModChain causes security and reliability on IIoT information by joining differential protection, Ethereum block chain and unified Machine learning. Consequently, security will be compromised and we use PriMod chain for giving protection and different compliances and created utilizing Python with attachment programming on essential PC.
James E. Short, Ken Miyachi, Christian D. Toouli, Steve Todd
The rapid upswing in interest in federated learning (FL) and federated analytics (FA) architectures has corresponded with the rapid increase in commercial AI software products, ranging from face detection and language translation to connected IOT devices, smartphones, and autonomous vehicles equipped with high-resolution sensors. However, the traditional client-server model does not readily address questions of data ownership, privacy, and data location in the context of the multiple datasets required for machine learning. In this paper, we report on a pilot distributed ledger and smart contract network model, designed to track analytic jobs in an HPC supercomputing environment. The test system design integrates the FL/FA model into a blockchain-based network architecture, wherein the test system records interactions with the global server and blockchain network. The design goal is to create a secure audit trail of supercomputer analytic operations and the ability to securely federate those operations across multiple supercomputer deployments. As there are still relatively few real-world applications of FL/FA models and blockchain networks in use, our system design, test deployment, and sample code are intended to provide interested researchers with exploratory tools for future research.
Digital Twin (DT) supports real time analysis and provides a reliable simulation platform in the Internet of Things (IoT). The creation and application of DT hinges on amounts of data, which poses pressure on the application of Artificial Intelligence (AI) for DT descriptions and intelligent decision-making. Federated Learning (FL) is a cutting-edge technology that enables geographically dispersed devices to collaboratively train a shared global model locally rather than relying on a data center to perform model training. Therefore, DT can benefit by combining with FL, successfully solving the ādata islandā problem in traditional AI. However, FL still faces serious challenges, such as enduring single-point failures, suffering from poison attacks, lacking effective incentive mechanisms. Before the successful deployment of DT, we should tackle the issues caused by FL. Researchers from industry and academia have recognized the potential of introducing Blockchain Technology (BT) into FL to overcome the challenges faced by FL, where BT acting as a distributed and immutable ledger, can store data in a secure, traceable, and trusted manner. However, to the best of our knowledge, a comprehensive literature review on this topic is still missing. In this paper, we review existing works about blockchain-enabled FL and visualize their prospects with DT. To this end, we first propose evaluation requirements with respect to security, fault-tolerance, fairness, efficiency, cost-saving, profitability, and support for heterogeneity. Then, we classify existing literature according to the functionalities of BT in FL and analyze their advantages and disadvantages based on the proposed evaluation requirements. Finally, we discuss open problems in the existing literature and the future of DT supported by blockchain-enabled FL, based on which we further propose some directions for future research.
Xi Peng, Xinglong Zhang, Lian Wang, Wenjuan Liu Ā· 5 authors
Medical data contains multiple records of patient data that are important for subsequent treatment and future research. However, it needs to be stored and shared securely to protect the privacy of the data. Blockchain is widely used in the management of healthcare data because of its decentralized and tamper-proof features. In order to study the development of blockchain in healthcare, this paper evaluates it from various perspectives. We analyze blockchain-based approaches from different application scenarios. These are blockchain-based electronic medical record sharing, blockchain and the Internet of Medical Things and blockchain-based federal learning. The results show that blockchain and smart contracts have a natural advantage in the field of medical data since they are tamper-proof and traceable. Finally, the challenges and future directions of blockchain in healthcare are discussed, which can help drive the field forward.