In this paper, we develop a foundation for a constellation of Low Earth Orbit (LEO) satellite IoT by constructing a Blockchain-based framework for continual knowledge sharing and learning collaboratively. This approach is directly applicable for a swarm of Unmanned Aerial Vehicles (UAVs). We ablate Federated Learning (FL) successful features as a basis to ensure high precision of learning inferences at timescales relevant to the underlying time-varying space network and channel dynamics. In such a dynamic setting, there is always a likelihood that miners may be compromised or fail to propagate information in time because of some intrinsic factors such as channel impairments, satellite handovers and attacks. Such transmission failures often lead to undesirable forking events in the Blockchain. Consequently, maintaining a low energy consumption and smallish delay in such an erratic network is highly nontrivial and challenging. To quantify the impacts of the forking and minimize the occurrence of such unwanted events and their adverse effects, we develop a procedure to estimate the expected energy consumption for a given set of miners, block transmissions, and LEOs’ or UAVs’ mobility. Besides, we shed light on deep learning-based resource allocation for mobile mining and demonstrate the synergic gain of FL with Blockchain.
Muhammad Nazmul Islam, Mubashir Husain Rehmani, Jinjun Chen
Permissioned blockchain such as Hyperledger fabric enables a secure supply chain model in Industrial Internet of Things (IIoT) through multichannel and private data collection mechanisms. Sharing of Industrial data including private data exchange at every stage between supply chain partners helps to improve product quality, enable future forecast, and enhance management activities. However, the existing data sharing and querying mechanism in Hyperledger fabric is not suitable for supply chain environment in IIoT because the queries are evaluated on actual data stored on ledger which consists of sensitive information such as business secrets, and special discounts offered to retailers and individuals. To solve this problem, we propose a differential privacy-based permissioned blockchain using Hyperledger fabric to enable private data sharing in supply chain in IIoT (DH-IIoT). We integrate differential privacy into the chaindcode (smart contract) of Hyperledger fabric to achieve privacy preservation. As a result, the query response consists of perturbed data which protects the sensitive information in the ledger. The proposed work (DH-IIoT) is evaluated by simulating a permissioned blockchain using Hyperledger fabric. We compare our differential privacy integrated chaincode of Hyperledger fabric with the default chaincode setting of Hyperledger fabric for supply chain scenario. The results confirm that the proposed work maintains 96.15% of accuracy in the shared data while guarantees the protection of sensitive ledger's data.
We propose three constructions of classically verifiable non-interactive zero-knowledge proofs and arguments (CV-NIZK) for QMA in various preprocessing models. - We construct a CV-NIZK for QMA in the quantum secret parameter model where a trusted setup sends a quantum proving key to the prover and a classical verification key to the verifier. It is information theoretically sound and zero-knowledge. - Assuming the quantum hardness of the learning with errors problem, we construct a CV-NIZK for QMA in a model where a trusted party generates a CRS and the verifier sends an instance-independent quantum message to the prover as preprocessing. This model is the same as one considered in the recent work by Coladangelo, Vidick, and Zhang (CRYPTO '20). Our construction has the so-called dual-mode property, which means that there are two computationally indistinguishable modes of generating CRS, and we have information theoretical soundness in one mode and information theoretical zero-knowledge property in the other. This answers an open problem left by Coladangelo et al, which is to achieve either of soundness or zero-knowledge information theoretically. To the best of our knowledge, ours is the first dual-mode NIZK for QMA in any kind of model. - We construct a CV-NIZK for QMA with quantum preprocessing in the quantum random oracle model. This quantum preprocessing is the one where the verifier sends a random Pauli-basis states to the prover. Our construction uses the Fiat-Shamir transformation. The quantum preprocessing can be replaced with the setup that distributes Bell pairs among the prover and the verifier, and therefore we solve the open problem by Broadbent and Grilo (FOCS '20) about the possibility of NIZK for QMA in the shared Bell pair model via the Fiat-Shamir transformation.
Homomorphic Encryption and Zero Knowledge Proofs are two trending concepts that are widely popular as data privacy preservation techniques in a wide variety of applications, especially in those associated with the newly evolved block chain technology which are immutable, distributed and secure. Zero knowledge proof is a cryptographic technique can provide proof that a certain statement is correct, without revealing any details about the statement, while homomorphic encryption allows to perform computations on encrypted data without decrypting it. This article explores the significance of the data privacy aspect provided by both ZKP and Homomorphic Encryption and how it can be effectively used to improvise the privacy of blockchain applications in various domains.\n\n
Sultan Algarni, Fathy Eassa, Khalid Ali Almarhabi, Abduallah Almalaise · 7 authors
The distributed nature of Internet of Things (IoT) and its rapid increase on a large scale raises many security and privacy issues. Access control is one of the major challenges currently addressed through centralized approaches that may rely on a third party and they are constrained by availability and scalability, which may result in a performance bottleneck. Therefore, this paper proposes a novel solution to manage the delivery of lightweight and decentralized secure access control of an IoT system based on a multi-agent system and a blockchain. The main objective of the proposed solution is to build Blockchain Managers (BCMs) for securing IoT access control, as well as allowing for secure communication between local IoT devices. Moreover, the solution also enables secure communication between IoT devices, fog nodes and cloud computing.
Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
The healthcare industry has witnessed significant transformations in e-health services by using mobile-edge computing (MEC) and blockchain to facilitate healthcare operations. Many MEC-blockchain-based schemes have been proposed, but some critical technical challenges still remain, such as low Quality of Services (QoS), data privacy, and system security vulnerabilities. In this article, we propose a new decentralized health architecture, called BEdgeHealth that integrates MEC and blockchain for data offloading and data sharing in distributed hospital networks. First, a data offloading scheme is proposed where mobile devices can offload health data to a nearby MEC server for efficient computation with privacy awareness. Moreover, we design a data-sharing scheme, which enables data exchanges among healthcare users by leveraging blockchain and interplanetary file system. Particularly, a smart contract-based authentication mechanism is integrated with MEC to perform decentralized user access verification at the network edge without requiring any central authority. The real-world experiment results and evaluations demonstrate the effectiveness of the proposed BEdgeHealth architecture in terms of improved QoS with data privacy and security guarantees, compared to the existing schemes.
T Kim, Seo-Joon Lee, Dong Jin Chang, Ja‐Wook Koo · 7 authors
Although blockchain is acknowledged as one of the most important technologies to lead the fourth industrial revolution, major technical challenges regarding security breach and privacy issues remain. This issue is particularly sensitive in applied medical fields where personal health information is handled within the network. In addition, contemporary blockchain-converged solutions do not consider restricted medical data regulations that are still obstacles in many countries worldwide. This implies a crucial need for a system or solution that is suitable for the healthcare sector. Therefore, this article proposes the development of a dynamic consent medical blockchain system called DynamiChain, based on a ruleset management algorithm for handling health examination data. Moreover, medical blockchain-related studies were systematically reviewed to prove the novelty of DynamiChain. The proposed system was implemented in a scenario where the exercise management healthcare company provided health management services based on data obtained from the data provider’s hospital. The proposed research is envisioned to provide a widely compatible blockchain medical system that could be applied in future healthcare fields.
Satyabrata Aich, Nday Kabulo Sinai, Saurabh Kumar, Mohammed Al Ali · 7 authors
For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and real-time usage.
Open access
2 source records
Privacy-Preserving Technologies in Data
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Mallikarjun Reddy Dorsala, V. N. Sastry, Chapram Sudhakar
With the advent of mobile crowdsensing, the mobile devices equipped with a variety of sensors (such as accelerometer, gyroscope, microphone etc.) are used to collect sensory data. A data aggregator processes the collected sensor data to deliver various services such as traffic management, health care and environmental monitoring. To ensure the privacy of the data, privacy-preserving aggregation (PPA) has attracted much attention since it can find aggregated statistics on the encrypted data. In this paper, we extend the existing PPA schemes in two directions: (1) Aggregator unforgeability – The aggregator performs the aggregation operation correctly. Although there are some schemes which consider aggregator unforgeability, they rely on cryptographic techniques. (2) Fair payments – The data owners receive the payments for their data contribution if and only if the aggregator receives the data. Contrary to existing works, we achieve the aggregator unforgeability and fair payments by modeling the aggregator as a smart contract running on a public Blockchain network. We design two PPA schemes FairNaivePPA and FairPPA for secure aggregation of MCS data with fair payments. We show the financial and transactional cost analysis of proposed contracts by implementing them in solidity and running them on Ethereum Blockchain.
With the rapid development of information technology, logistics systems are developing towards intelligence. The Internet of Things (IoT) devices throughout the logistics network could provide strong support for smart logistics. However, due to the limited computing and storage resources of IoT devices, logistics data with user sensitive information are generally stored in a centralized cloud center, which could easily cause privacy leakage. In this paper, we propose Logisticschain, a blockchain-based secure storage scheme for logistics data. In this scheme, the sensing data from IoT devices should be encrypted for fine-grained access control, and a customized blockchain structure is proposed to improve the storage efficiency of systems. Also, an efficient consensus mechanism is introduced to improve the efficiency of the consensus process in the blockchain. Specific to the logistics process, the sensing data generated from IoT devices will be encrypted and aggregated into the blockchain to ensure data security. Moreover, the stored logistics records can be securely audited by leveraging the blockchain network; both IoT data and logistics demands cannot be deleted or tampered to avoid disputes. Finally, we analyze the security and privacy properties of our Logisticschain and evaluate its performance in terms of computational costs by developing an experimental platform.
Increasingly, information systems rely on computational, storage, and network resources deployed in third-party facilities such as cloud centers and edge nodes. Such an approach further exacerbates cybersecurity concerns constantly raised by numerous incidents of security and privacy attacks resulting in data leakage and identity theft, among others. These have, in turn, forced the creation of stricter security and privacy-related regulations and have eroded the trust in cyberspace. In particular, security-related services and infrastructures, such as Certificate Authorities (CAs) that provide digital certificate services and Third-Party Authorities (TPAs) that provide cryptographic key services, are critical components for establishing trust in crypto-based privacy-preserving applications and services. To address such trust issues, various transparency frameworks and approaches have been recently proposed in the literature. This paper proposes TAB framework that provides transparency and trustworthiness of third-party authority and third-party facilities using blockchain techniques for emerging crypto-based privacy-preserving applications. TAB employs the Ethereum blockchain as the underlying public ledger and also includes a novel smart contract to automate accountability with an incentive mechanism that motivates users to participate in auditing, and punishes unintentional or malicious behaviors. We implement TAB and show through experimental evaluation in the Ethereum official test network, Rinkeby, that the framework is efficient. We also formally show the security guarantee provided by TAB, and analyze the privacy guarantee and trustworthiness it provides.
The digitization, informatization, and intelligentization of physical systems require strong support from big data analysis. However, due to restrictions on data security and privacy and concerns about the cost of big data collection, transmission, and storage, it is difficult to do data aggregation in real-world power systems, which directly retards the effective implementation of smart grid analytics. Federated learning, an advanced distributed learning method proposed by Google, seems a promising solution to the above issues. Nevertheless, it relies on a server node to complete model aggregation and the framework is limited to scenarios where data are independent and identically distributed. Thus, we here propose a serverless distributed learning platform based on blockchain to solve the above two issues. In the proposed platform, the task of machine learning is performed according to smart contracts, and encrypted models are aggregated via a mechanism of knowledge distillation. Through this proposed method, a server node is no longer required and the learning ability is no longer limited to independent and identically distributed scenarios. Experiments on a public electrical grid dataset will verify the effectiveness of the proposed approach.
P. Chinnasamy, B. Vinodhini, V. Praveena, C. Vinothini · 5 authors
Abstract Internet-of-Things and Blockchain technology are evolving all around today’s modern world to solve many problems such as security, communications, data collection and analysis, etc. However, we also have some issues, such as efficient data sharing, restriction of access, reliable authentication, etc. The proposed framework is designed to solve the problems related to security and authorization in IoT network access control. In addition, the system’s aim is to accomplish security, authorizing, and encryption for information exchange through IoT networks. In this article, a novel system is introducing to provide the data sharing system that integrates blockchain based access control system for IoT devices. Here, we are creating three different smart contracts to offers an efficient access control management like contract to provide access control, contract to provide authentication, and contract to provide a judgment. Finally, the effectiveness of the proposed approach is measured against the cost consumption of smart contracts and cryptographic functions. The cost utilization comparison is carried out against certain current approaches as well as the findings clearly demonstrate that the proposed approach is cost-effective.
Over the past decade, blockchains and distributed ledger technologies have rapidly evolved. With increasing transaction volumes and the proliferation of decentralized applications based on smart contracts, a need for a deeper understanding arises. We structure the field that we term distributed ledger analytics.
The development of educational informatization makes data privacy particularly important in education. With society's development, the education system is complicated, and the result of education evaluation becomes more and more critical to students. The evaluation process of education must be justice and transparent. In recent years, the Onscreen Marking (OSM) system based on traditional cloud platforms has been widely used in various large-scale public examinations. However, due to the excessive concentration of power in the existing scheme, the mainstream marking process is not transparent, and there are hidden dangers of black-box operation, which will damage the fairness of the examination. In addition, issues related to data security and privacy are still considered to be severe challenges. This paper deals with the above problems by providing secure and private transactions in a distributed OSM assuming the semi-trusted examination center. We have implemented a proof-of-concept for a consortium blockchain-based OSM in a privacy-preserving and auditable manner, enabling markers to mark on the distributed ledger anonymously. We have proposed a distributed OSM system in high-level, which provides theoretical support for the fair evaluation process of education informatization. It has particular theoretical and application value for education combined with blockchain.
Cong T. Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Yong Xiao · 7 authors
In this article, we propose FedChain, a novel framework for federated-blockchain systems, to enable effective transferring of tokens between different blockchain networks. Particularly, we first introduce a federated-blockchain system together with a cross-chain transfer protocol to facilitate the secure and decentralized transfer of tokens between chains. We then develop a novel PoS-based consensus mechanism for FedChain, which can satisfy strict security requirements, prevent various blockchain-specific attacks, and achieve a more desirable performance compared to those of other existing consensus mechanisms. Moreover, a Stackelberg game model is developed to examine and address the problem of centralization in the FedChain system. Furthermore, the game model can enhance the security and performance of FedChain. By analyzing interactions between the stakeholders and chain operators, we can prove the uniqueness of the Stackelberg equilibrium and find the exact formula for this equilibrium. These results are especially important for the stakeholders to determine their best investment strategies and for the chain operators to design the optimal policy to maximize their benefits and security protection for FedChain. Simulations results then clearly show that the FedChain framework can help stakeholders to maximize their profits and the chain operators to design appropriate parameters to enhance FedChain's security and performance.
Federated Learning is a promising machine learning paradigm when multiple parties collaborate to build a high-quality machine learning model. Nonetheless, these parties are only willing to participate when given enough incentives, such as a fair reward based on their contributions. Many studies explored Shapley value based methods to evaluate each party's contribution to the learned model. However, they commonly assume a semi-trusted server to train the model and evaluate the data owners' model contributions, which lacks transparency and may hinder the success of federated learning in practice. In this work, we propose a blockchain-based federated learning framework and a protocol to transparently evaluate each participant's contribution. Our framework protects all parties' privacy in the model building phase and transparently evaluates contributions based on the model updates. The experiment with the handwritten digits dataset demonstrates that the proposed method can effectively evaluate the contributions.
Reputation expresses the beliefs or opinions about someone or something that are held by an individual or by a community. Reputation Management Systems (RMSs) handle representation, computation, and storage of reputation in some quantitative form, suitable for grounding trust relations among parties. Quantifying reputation is important in situations, like online service provision, which involve interaction between parties who do not know (and potentially distrust) each other. The basic idea is to let parties rate each other. When a party is considered for interaction, its ratings can be aggregated in order to derive a score for deciding whether to trust it or not. While much valuable research work has been done on reputation-based trust schemes, the problem of establishing collective trust in the reputation management system itself has never been fully solved. Recently, several researchers have put forward the idea of using Distributed Ledger Technology (DLT) as the foundation for implementing trustworthy RMSs. The purpose of this paper is to identify some critical problems that arise when DLTs are used in order to manage evidence about previous interaction and compute reputations. The paper proposes some practical solutions and describes methods to deploy them on top of standard DLT of the Ethereum family.
Hao Xu, Zihan Zhou, Lei Zhang, Yunqing Sun · 5 authors
As 6G networks evolve towards a synergistic system of Communication, Sensing, and Computing, Radio Access Networks become more distributed, necessitating robust end-to-end authentication. We propose Blockchain-enabled Radio Access Networks, a novel decentralized RAN architecture enhancing security, privacy, and efficiency in authentication processes. BE-RAN leverages distributed ledger technology to establish trust, offering user-centric identity management, enabling mutual authentication, and facilitating on-demand point-to-point inter-network elements and UE-UE communication with accountable logging and billing service add-on for public network users, all without relying on centralized authorities. We envision a thoroughly decentralized RAN model and propose a privacy-preserving P2P communication approach that complements existing security measures while supporting the CSC paradigm. Results demonstrate BE-RAN significantly reduces communication and computation overheads, enhances privacy through decentralized identity management, and facilitates CSC integration, advancing towards more efficient and secure 6G networks.
Federated learning is a branch of machine learning where a shared model is created in a decentralized and privacy-preserving fashion, but existing approaches using blockchain are limited by tailored models. We consider the possibility to extend a set of supported models by introducing the oracle service and exploring the usability of blockchain-based architecture. The investigated architecture combines an oracle service with a Hyperledger Fabric chaincode. We compared two logistic regression implementations in Go language—a pure chaincode and an oracle service—at various data (2–32 k instances) and network (3–13 peers) sizes. Experiments were run to assess the performance of blockchain-based model inference using 2D synthetic and EEG eye state datasets for a supervised machine learning detection task. The benchmarking results showed that the impact on performance is acceptable with the median overhead of oracle service reaching 2–4%, depending on the dimensionality of the dataset. The overhead tends to diminish at large dataset sizes with the runtime depending on the network size linearly, where additional peers increased the runtime by 6.3 and 6.6 s for 2D and EEG datasets, respectively. Demonstrated negligible difference between implementations justifies the flexible choice of model in the blockchain-based federated learning and other machine learning applications.
Smart contract risk can be defined as a financial risk of loss due to cyber attacks on or contagious failures of smart contracts. Its quantification is of paramount importance to technology platform providers as well as companies and individuals when considering the deployment of this new technology. That is why, as our primary contribution, we propose a structural framework of aggregate loss distribution for smart contract risk under the assumption of a tree-stars graph topology representing the network of interactions among smart contracts and their users. Up to our knowledge, there exist no theoretical frameworks or models of an aggregate loss distribution for smart contracts in this setting. To achieve our goal, we contextualize the problem in the probabilistic graph-theoretical framework using bond percolation models. We assume that the smart contract network topology is represented by a random tree graph of finite size, and that each smart contract is the center of a {random} star graph whose leaves represent the users of the smart contract. We allow for heterogeneous loss topology superimposed on this smart contract and user topology and provide analytical results and instructive numerical examples.
Jun Li, Yumeng Shao, Kang Wei, Ming Ding · 8 authors
Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for model aggregation, standard FL is vulnerable to server malfunctions, untrustworthy servers, and external attacks. To address these issues, we propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL). In a round of the proposed BLADE-FL, each client broadcasts its trained model to other clients, aggregates its own model with received ones, and then competes to generate a block before its local training on the next round. We evaluate the learning performance of BLADE-FL, and develop an upper bound on the global loss function. Then we verify that this bound is convex with respect to the number of overall aggregation rounds$K$, and optimize the computing resource allocation for minimizing the upper bound. We also note that there is a critical problem of training deficiency, caused by lazy clients who plagiarize others’ trained models and add artificial noises to disguise their cheating behaviors. Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal$K$, the learning parameters, and the proportion of lazy clients. Based on the MNIST and Fashion-MNIST datasets, we see that the experimental results are consistent with the analytical ones. To be specific, the gap between the developed upper bound and experimental results is lower than$5\%$, and the optimized$K$based on the upper bound can effectively minimize the loss function.