Rodrigo Dutra Garcia, Gowri Ramachandran, Raja Jurdak, Jó Ueyama
Real-world applications in healthcare and supply chain domains produce, exchange, and share data in a multi-stakeholder environment. Data owners want to control their data and privacy in such settings. On the other hand, data consumers demand methods to understand when, how, and who produced the data. These requirements necessitate data governance frameworks that guarantee data provenance, privacy protection, consent management, and selective disclosure. We introduce a decentralized data governance framework based on blockchain technology, proxy re-encryption, and Boneh, Boyen, and Shacham (BBS) signatures to let data owners control, selectively share and track their data through privacy-enhancing, consent management, and selective disclosure mechanisms. Besides, our framework allows the data consumers to understand data lineage through a blockchain-based provenance mechanism. We use Digital medical e-prescription as the use case since it handles sensitive data in a multi-stakeholder environment while showing how the medical community can manage patients’ sensitive prescription data, involving patients as data owners, and doctors, and pharmacists as data consumers. Our proof-of-concept implementation and evaluation results based on CosmWasm, Hyperledger Besu, Ethereum, pyUmbral PRE, and BBS signatures show that the proposed decentralized system is platform-agnostic, scalable and guarantees a higher degree of transparency, privacy, and trust with minimal overhead.
To enable precision medicine and remote patient monitoring, internet of healthcare things (IoHT) has gained significant interest as a promising technique. With the widespread use of IoHT, nonetheless, privacy infringements such as IoHT data leakage have raised serious public concerns. On the other side, blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems. In this survey, a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection. In addition, various types of privacy challenges in IoHT are identified by examining general data protection regulation (GDPR). More importantly, an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented. Finally, several challenges in four promising research areas for blockchain-based IoHT systems are pointed out, with the intent of motivating researchers working in these fields to develop possible solutions.
Compared with traditional paper-based medical records, electronic health records (EHRs) are widely used because of their efficiency, security, and reducing data redundancy. However, EHRs still manifest poor interoperability and privacy issues are unresolved. As a distributed ledger protocol composed of encrypted blocks of data organized in chains, blockchain represents a potential tool to solve the shortcomings of EHRs in terms of interoperability and privacy. In this paper, we define EHRs and blockchain technology and introduce several classic schemes based on blockchain technology to strengthen EHR interoperability and privacy protection. We then review ongoing challenges in the areas of data management efficiency, fairness of access, and trust in the systems. In this commentary, we suggest ongoing research needs for health informatics, data sciences, and ethics to establish EHRs based on blockchain technology. Blockchain-based EHR schemes must address the potential inequality of healthcare resources, the huge carbon footprint of computational needs, and potential distrust of health providers and patients that may ensue with wider use of blockchain technology.
Continuously generated volumes of health data make healthcare a data-intensive domain. This data needs to be collected, stored, and shared among different healthcare actors for various purposes, such as reporting, analysis, collaborative research, and personalized healthcare services. However, the existing data storage and exchange solutions in the healthcare domain exhibit several challenges related to, e.g., data security, patient privacy, and interoperability. Recently, the industry and research community turned its focus to the possible use of blockchain technology to solve some of these challenges in the healthcare domain. The blockchain technology along with the support from smart contracts is considered a salient facilitator for secure and efficient health data sharing. This is due to its unique features, such as decentralization, trustlessness, immutability, traceability, and transparency. In this paper, we provide a comprehensive survey of the state-of-the-art efforts that envision the use of blockchain-based solutions in the healthcare domain. To this end, we introduce a systematic framework for classifying and analyzing such systems. The framework consists of classification in several dimensions: interactions between healthcare entities, functional components of healthcare storage systems, challenges in the healthcare domain that can be overcome by using the blockchain technology, and benefits for healthcare storage systems derived from the fundamental features of the technology. When analyzing over 40 systems and solutions proposed in the state-of-the-art, we perform their rigorous placement by identifying the exact scope of each solution and mapping it to the above taxonomies of interactions, functional components, challenges, and benefits. We additionally provide an extensive discussion of compliance with privacy-related regulations of General Data Protection Regulation (GDPR) in EU, and Health Insurance Portability and Accountability Act (HIPAA). Following the results of the analysis, we have outlined a number of important research gaps and future directions yet to be addressed.
Specializing Directed Acyclic Graph Federated Learning (SDAGFL) is a new federated learning framework with the advantages of decentralization, personalization, resisting a single point of failure, and poisoning attack. Instead of training a single global model, the clients in SDAGFL update their models asynchronously from the devices with similar data distribution through Directed Acyclic Graph Distributed Ledger Technology (DAG-DLT), which is designed for IoT scenarios. Because of many the features inherited from DAG-DLT, SDAGFL is suitable for IoT scenarios in many aspects. However, the training process of SDAGFL is quite energy consuming, in which each client needs to compute the confidence and rating of the nodes selected by multiple random walks by traveling the ledger with 15–25 depth to obtain the “reference model” to judge whether or not to broadcast the newly trained model. As we know, the energy consumption is an important issue for IoT scenarios, as most devices are battery-powered with strict energy restrictions. To optimize SDAGFL for IoT, an energy-efficient SDAGFL based on an event-triggered communication mechanism, i.e., ESDAGFL, is proposed in this paper. In ESDAGFL, the new model is broadcasted only in the event that the new model is significantly different from the previous one, instead of traveling the ledger to search for the “reference model”. We evaluate the ESDAGFL on the FMNIST-clustered and Poets dataset. The simulation is performed on a platform with Intel®CoreTM i7-10700 CPU (CA, USA). The simulation results demonstrate that ESDAGFL can reach a balance between training accuracy and specialization as good as SDAGFL. What is more, ESDAGFL can reduce the energy consumption by 42.5% and 51.7% for the FMNIST-clustered and Poets datasets, respectively.
Cooperative perception is an essential and widely discussed application of connected automated vehicles. However, the authenticity of perception data is not ensured, because the vehicles cannot independently verify the event they did not see. Many methods, including trust-based (i.e., statistical) approaches and plausibility-based methods, have been proposed to determine data authenticity. However, these methods cannot verify data without a priori knowledge. In this study, a novel approach of constructing a self-proving data from the number plate of target vehicles was proposed. By regarding the pseudonym and number plate as a shared secret and letting multiple vehicles prove they know it independently, the data authenticity problem can be transformed to a cryptography problem that can be solved without trust or plausibility evaluations. Our work can be adapted to the existing works including ETSI/ISO ITS standards while maintaining backward compatibility. Analyses of common attacks and attacks specific to the proposed method reveal that most attacks can be prevented, whereas preventing some other attacks, such as collusion attacks, can be mitigated. Experiments based on realistic data set show that the rate of successful verification can achieve 70\% to 80\% at rush hours.
Shailendra Sharma, Bonthu Kotaiah, Samarth Singh, K. V. Daya Sagar · 6 authors
Recent developments in deep learning techniques have produced significant improvements in long-standing AI jobs like drug discovery, gene analysis, and speech and image recognition. Although deep learning has numerous benefits, the identical training dataset that has did it so reliable also raises serious privacy concerns to address these privacy concerns, McMahan et al. created Federated Deep Learning, a together distributed deep learning paradigm for the mobile devices (FDL). Deep learning and distributed computation are essentially combined in FDL, where several parties get involved into the process of distributed training and parameter server records track of a deep learning model that needs to be built. The central parameter server first distributes a pre-trained model globally on a common set of data to each participant. Then, in each cycle, each party utilizes a local dataset to the train and improve the current global model. The gradients are gathered from each party by the central parameter server, which then utilises them to build a new global oriented model for upcoming iteration. At final stage the different parties and the central parameter of server repeat the aforementioned procedure till the global modelling attains a particular accuracy or ideal convergence.
The audit log is a crucial component to monitor fine-grained operations over sensitive data (e.g., personal, health) for security inspection and assurance. Since such data operations can be highly sensitive, it is vital to ensure that the audit log achieves not only validity and immutability, but also confidentiality against active threats to standard data regulations (e.g., HIPAA) compliance. Despite its critical needs, state-of-the-art privacy-preserving audit log schemes (e.g., Ghostor (NSDI '20), Calypso (VLDB '19)) do not fully obtain a high level of privacy, integrity, and immutability simultaneously, in which certain information (e.g., user identities) is still leaked in the log. In this paper, we propose Harpocrates, a new privacy-preserving and immutable audit log scheme. Harpocrates permits data store, share, and access operations to be recorded in the audit log without leaking sensitive information (e.g., data identifier, user identity), while permitting the validity of data operations to be publicly verifiable. Harpocrates makes use of blockchain techniques to achieve immutability and avoid a single point of failure, while cryptographic zero-knowledge proofs are harnessed for confidentiality and public verifiability. We analyze the security of our proposed technique and prove that it achieves non-malleability and indistinguishability. We fully implemented Harpocrates and evaluated its performance on a real blockchain system (i.e., Hyperledger Fabric) deployed on a commodity platform (i.e., Amazon EC2). Experimental results demonstrated that Harpocrates is highly scalable and achieves practical performance.
BACKGROUND: Collaborative privacy-preserving modeling across several healthcare institutions allows for the construction of more generalizable predictive models while protecting patient privacy. OBJECTIVE: We aim at addressing the site availability issue on a hierarchical network by designing an immutable/transparent/source-verifiable quorum mechanism. METHODS: We developed an approach to combine a hierarchical learning algorithm, a novel Proof-of-Quorum (PoQ) consensus protocol, and a design of blockchain smart contracts. We constructed QuorumChain as an example and evaluated the scenarios of site-unavailability during the initialization and/or iteration phases of the modeling process on three healthcare/genomic datasets. RESULTS: When one or more sites would become unavailable, HierarchicalChain could not function, whereas QuorumChain improved predictive correctness significantly (the full Area Under the receiver operating characteristic Curve, or AUC, improved from 0.068 to 0.441, all with p-values < 0.001). CONCLUSION: By constructing a quorum to continue the modeling process, QuorumChain possesses the capability to tackle the situation of sites being unavailable. It inherits the capability of learning on network-of-networks, improves learning continuity, and provides data/software immutability, transparency, and provenance, which can be important in expediting clinical research.
Carsten Baum, Robin Jadoul, Emmanuela Orsini, Peter Schöll · 5 authors
Zero-Knowledge protocols have increasingly become both popular and practical in recent years due to their applicability in many areas such as blockchain systems. Unfortunately, public verifiability and small proof sizes of zero-knowledge protocols currently come at the price of strong assumptions, large prover time, or both, when considering statements with millions of gates. In this regime, the most prover-efficient protocols are in the designated verifier setting, where proofs are only valid to a single party that must keep a secret state.
Federated learning (FL) is a promising way to allow multiple data owners (clients) to collaboratively train machine learning models without compromising data privacy. Yet, existing FL solutions usually rely on a centralized aggregator for model weight aggregation, while assuming clients are honest. Even if data privacy can still be preserved, the problem of single-point failure and data poisoning attack from malicious clients remains unresolved. To tackle this challenge, we propose to use distributed ledger technology (DLT) to achieve FLock, a secure and reliable decentralized Federated Learning system built on blockchain. To guarantee model quality, we design a novel peer-to-peer (P2P) review and reward/slash mechanism to detect and deter malicious clients, powered by on-chain smart contracts. The reward/slash mechanism, in addition, serves as incentives for participants to honestly upload and review model parameters in the FLock system. FLock thus improves the performance and the robustness of FL systems in a fully P2P manner.
Tharindu Ranathunga, Alan McGibney, Susan Rea, Sourabh Bharti
Traditional federated learning (FL) adopts a client-server architecture where FL clients (e.g., IoT edge devices) train a common global model with the help of a centralized orchestrator (cloud server). However, current approaches are moving away from centralized orchestration toward a decentralized one in order to fully adapt FL for a cross-silo configuration with multiple organizations acting as clients. State-of-the-art decentralized FL mechanisms make at least one of the following assumptions: 1) clients are trusted organizations and cannot inject low-quality model updates for aggregation and 2) client local models can be shared with other clients or a third party for verification of low-quality updates. This article proposes a Blockchain-based decentralized framework for scenarios where participatory organizations are believed to be fully capable of injecting low-quality model updates as they are not willing to expose their local models to any other entity for verification purpose. The proposed decentralized FL framework adopts a novel hierarchical network of aggregators with the ability to punish/reward organizations in proportion to their local model quality updates. The framework is flexible and unlike state-of-the-art solutions, prevents a single entity from possessing the aggregated model in any FL round of training. The proposed framework is tested with respect to off-chain and on-chain performance in two Industry 4.0 use cases: 1) predictive maintenance and 2) product visual inspection. A comparative evaluation against the state-of-the-art reveals the proposed framework’s utility in terms of minimizing model convergence time and latency while maximizing accuracy and throughput.
In recent years, with 5G and other key technology breakthroughs, IoT technology is accelerating penetration into various industries, and the era of Internet of Everything is coming. However, while the industry is promoting the rapid development of industrial IoT, IoT security issues are also coming up. Based on this, this paper proposes a data privacy protection method for the smart IoT platform, which first adopts zero-knowledge identity proof technology to complete the identity access authentication of a large number of IoT terminal devices; then the data is downscaled and aggregated in the intermediate data collection device, and then the processed data is added to the noise based on Laplace distribution, and finally the data is transmitted to the main server, thus realizing the smart IoT platform Data privacy protection in the process of data transmission.
Suhair Alshehri, Omaimah Bamasaq, Daniyal Alghazzawi, Arwa A. Jamjoom
The Internet of Things (IoT) is vulnerable to leakage of private information during data sharing. To avoid this problem, access control and secure data sharing have been introduced in IoT; however, many challenges are faced because of centralized access control and single delegator selection. Additionally, blockchain is integrated into IoT to enhance the security of the environment. For that purpose, this research proposes dynamic secure access control using the blockchain (DSA-Block) model, which performs secure access control and data sharing. Initially, the IoT device attributes and user attributes are registered at a local domain authority (LDA) for generating private and public keys using the hyperelliptic curve cryptography (HECC) algorithm, which ensures the legitimacy of the users and devices. Then, the IoT devices send a request message to the edge nodes (ENs) via a gateway, which performs request filtration by validating the user’s authenticity. The filtered requests are sent to the edge server to perform access delegation using rock hyraxes swarm optimization (RHSO), which selects a set of delegator nodes. The access control decision is made by using the Trusted practical Byzantine fault tolerance (PBFT) consensus algorithm. The IoT data are stored in the cloud server for secure storage, in which the data are secured using a differential privacy mechanism. Finally, dual revocations, such as user attribute revocation and user revocation, are used to maintain security. The performance of DSA-Block is evaluated and the results demonstrate that the proposed DSA-Block model achieves superior performance compared to previous works.
Rajesh Kumar, Jay Kumar, Abdullah Aman Khan, Zakria Zakria · 8 authors
Medical healthcare centers are envisioned as a promising paradigm to handle the massive volume of data for COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and training models within a single organization. This practice can be considered a weakness as it leads to several privacy and security concerns related to raw data communication. To overcome this weakness and secure raw data communication, we propose a blockchain-based federated learning framework that provides a solution for collaborative data training. The proposed framework enables the coordination of multiple hospitals to train and share encrypted federated models while preserving data privacy. Blockchain ledger technology provides decentralization of federated learning models without relying on a central server. Moreover, the proposed homomorphic encryption scheme encrypts and decrypts the gradients of the model to preserve privacy. More precisely, the proposed framework: (i) train the local model by a novel capsule network for segmentation and classification of COVID-19 images, (ii) furthermore, we use the homomorphic encryption scheme to secure the local model that encrypts and decrypts the gradients, (iii) finally, the model is shared over a decentralized platform through the proposed blockchain-based federated learning algorithm. The integration of blockchain and federated learning leads to a new paradigm for medical image data sharing over the decentralized network. To validate our proposed model, we conducted comprehensive experiments and the results demonstrate the superior performance of the proposed scheme.
Veronika Stephanie, Ibrahim Khalil, Mohammed Atiquzzaman, Xun Yi
The advancement of internet and communication technologies has led to the era of Industry 4.0. This shift is followed by healthcare industries creating the term Healthcare 4.0. In Healthcare 4.0, the use of Internet of Things-enabled medical imaging devices for early disease detection has enabled medical practitioners to increase healthcare institutions' quality of service. However, Healthcare 4.0 is still lagging in artificial intelligence and big data compared to other Industry 4.0 due to data privacy concerns. In addition, institutions' diverse storage and computing capabilities restrict institutions from incorporating the same training model structure. This article presents a secure multiparty computation-based ensemble federated learning with blockchain that enables heterogeneous models to collaboratively learn from healthcare institutions' data without violating users' privacy. Blockchain properties also allow the party to enjoy data integrity without trust in a centralized server while also providing each healthcare institution with auditability and version control capability.
Jin Wang, Wei Ou, Osama Alfarraj, Amr Tolba · 6 authors
Since transactions in blockchain are based on public ledger verification, this raises security concerns about privacy protection. And it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification, when the whole transaction on the chain is verified. In order to improve the efficiency and privacy protection of block data verification, this paper proposes an efficient block verification mechanism with privacy protection based on zero-knowledge proof (ZKP), which not only protects the privacy of users but also improves the speed of data block verification. There is no need to put the whole transaction on the chain when verifying block data. It just needs to generate the ZKP and root hash with the transaction information, then save them to the smart contract for verification. Moreover, the ZKP verification in smart contract is carried out to realize the privacy protection of the transaction and efficient verification of the block. When the data is validated, the buffer accepts the complete transaction, updates the transaction status in the cloud database, and packages up the chain. So, the ZKP strengthens the privacy protection ability of blockchain, and the smart contracts save the time cost of block verification.
The sharing of high-quality traffic information plays a crucial role in enhancing the driving experience and safety performance for vehicular networks, especially in the development of electric vehicles (EVs). The crowdsourcing-based real-time navigation of charging piles is characterized by low delay and high accuracy. However, due to the lack of an effective incentive mechanism and the resource-consuming bottleneck of sharing real-time road conditions, methods to recruit or motivate more EVs to provide high-quality information gathering has attracted considerable interest. In this paper, we first introduce a blockchain platform, where EVs act as the blockchain nodes, and a reputation-based incentive mechanism for vehicular networks. The reputations of blockchain nodes are calculated according to their historical behavior and interactions. Further, we design and implement algorithms for updating honest-behavior-based reputation as well as for screening low-reputation miners, to optimize the profits of miners and address spatial crowdsourcing tasks for sharing information on road conditions. The experimental results show that the proposed reputation-based incentive method can improve the reputation and profits of vehicle users and ensure data timeliness and reliability.
Richard Blythman, Mohamed Arshath, Jakub Smékal, Hithesh Shaji · 6 authors
AI requires heavy amounts of storage and compute. As a result, AI developers are regular users of centralised cloud services such as AWS, GCP and Azure, compute environments such as Jupyter and Colab notebooks, and AI Hubs such as HuggingFace and ActiveLoop. There services are associated with certain benefits and limitations that stem from the underlying infrastructure and governance systems with which they are built. These limitations include high costs, lack of monetization and reward, lack of control and difficulty of reproducibility. At the same time, there are few libraries that allow data scientists to interact with decentralised storage in the language that data scientists are used to, and few hubs where they can discover and interact with AI assets. In this report, we explore the potential of decentralized technologies - such as Web3 wallets, peer-to-peer marketplaces, decentralized storage (IPFS and Filecoin) and compute, and DAOs - to address some of the above limitations. We showcase some of the libraries and integrations that we have built to tackle these issues, as well as a proof of concept of a decentralized AI Hub app, that all use IPFS as a core infrastructural component.
Federated learning is a type of privacy-preserving, collaborative machine learning. Instead of sharing raw data, the federated learning process cooperatively exchanges the model parameters and aggregates them in a decentralized manner through multiple users. In this study, we designed and implemented a hierarchical blockchain system using a public blockchain for a federated learning process without a trusted curator. This prevents model-poisoning attacks and provides secure updates of a global model. We conducted a comprehensive empirical study to characterize the performance of federated learning in our testbed and identify potential performance bottlenecks, thereby gaining a better understanding of the system.
We study the problem of simultaneously addressing both ballot stuffing and participation privacy for pollsite voting systems. Ballot stuffing is the attack where fake ballots (not cast by any eligible voter) are inserted into the system. Participation privacy is about hiding which eligible voters have actually cast their vote. So far, the combination of ballot stuffing and participation privacy has been mostly studied for internet voting, where voters are assumed to own trusted computing devices. Such approaches are inapplicable to pollsite voting where voters typically vote bare handed. We present an eligibility audit protocol to detect ballot stuffing in pollsite voting protocols. This is done while protecting participation privacy from a remote observer - one who does not physically observe voters during voting. Our protocol can be instantiated as an additional layer on top of most existing pollsite E2E-V voting protocols. To achieve our guarantees, we develop an efficient zero-knowledge proof (ZKP), that, given a value $v$ and a set $Φ$ of commitments, proves $v$ is committed by some commitment in $Φ$, without revealing which one. We call this a ZKP of reverse set membership because of its relationship to the popular ZKPs of set membership. This ZKP may be of independent interest.
The Internet of Medical Things (IOMT) is critical in improving electronic device precision, dependability, and productivity. Researchers are driving the development of digital healthcare systems by connecting available medical resources and healthcare services. However, there are concerns about the security of sharing patients’ electronic health records. In response to the prevailing problems such as difficulties in sharing medical records between different hospitals and patients’ inability to grasp the usage of their medical records, we propose a patient-controlled and cloud-chain collaborative multi-authority attribute-based encryption for EHR sharing with verifiable outsourcing decryption and hiding access policies (VO-PH-MAABE). This scheme uses blockchain to store the validation parameters by utilizing its immutable, which data users use to verify the correctness of third-party outsourcing decryption results. In addition, we use policy-hiding technology to protect data privacy so that data security is guaranteed. Moreover, we use blockchain technology to establish trust among multiple authorities and utilize Shamir secret sharing and smart contracts to compute keys or tokens for attributes managed across multiple administrative domains, which avoids a single point of failure and reduces communication and computation overhead on the data user side. Finally, the ciphertext indistinguishability security under the chosen plaintext attack is demonstrated under the stochastic prediction model and compared with other schemes in terms of functionality, communication overhead, and computation overhead. The experimental results show the effectiveness of this scheme.
Metaverse has rekindled human beings' desire to further break space-time barriers by fusing the virtual and real worlds. However, security and privacy threats hinder us from building a utopia. A metaverse embraces various techniques, while at the same time inheriting their pitfalls and thus exposing large attack surfaces. Blockchain, proposed in 2008, was regarded as a key building block of metaverses. it enables transparent and trusted computing environments using tamper-resistant decentralized ledgers. Currently, blockchain supports Decentralized Finance (DeFi) and Non-fungible Tokens (NFT) for metaverses. However, the power of a blockchain has not been sufficiently exploited. In this article, we propose a novel trustless architecture of blockchain-enabled metaverse, aiming to provide efficient resource integration and allocation by consolidating hardware and software components. To realize our design objectives, we provide an On-Demand Trusted Computing Environment (OTCE) technique based on local trust evaluation. Specifically, the architecture adopts a hypergraph to represent a metaverse, in which each hyperedge links a group of users with certain relationship. Then the trust level of each user group can be evaluated based on graph analytics techniques. Based on the trust value, each group can determine its security plan on demand, free from interference by irrelevant nodes. Besides, OTCEs enable large-scale and flexible application environments (sandboxes) while preserving a strong security guarantee.
Kamal Saluja, Sunil Gupta, Amit Vajpayee, Sanjoy Kumar Debnath · 6 authors
End users are now encircled by an ever-increasing volume of information from edge devices relevant to a range of stakeholders, thanks to the introduction of edge computing in a variety of application domains. However, because of their distrust, these edge devices are unable to communicate significant amounts of data. The non-repudiation and non-tampering features of the block chain are used in this study to provide trust in collaborative edges. To address the limited processing capabilities of edge devices, create a block chain-based huge data sharing architecture in cooperative edges. Then, for high computational reduction, propose a Proof-of-Collaboration consensus technique, in which edge devices participate in block formation by giving their PoC credits. Furthermore, a useless transaction filter technique was proposed for transaction offloading, drastically decreasing the block chain's storage space in edges. Comprehensive tests are carried out to illustrate our proposal's better performance. Using this learning opportunity an environment friendly block chain sustainable infrastructure be created for developing countries.