Controlled drugs are drugs that when used ethically may prove life-saving for patients but these drugs also have the potential for abuse, misuse, and addiction. Controlled drugs are under strict government regulations, but still few people manage to diverge these drugs from the ethical system. Few recent techniques that are considered for administration of controlled drugs are Electronic Health Records (EHR), Machine Learning and Advanced Analytics, IOT, etc. But the techniques mentioned above have some disadvantages such as EHR is centralized, ML requires high computing power and is less scalable, IOT devices are expensive and are vulnerable to cyber-attacks thus compromising sensitive information. The system proposed in this paper uses an Ethereum blockchain for administration of controlled drugs. Blockchain ensures transparency, traceability, immutability, decentralization and security in the system.
Shi Peng Li, Jingwei Li, Yuxing Tang, Xiapu Luo · 12 authors
Today's blockchain systems store detailed runtime information in the format of transactions and blocks, which are valuable not only to understand the finance of blockchain-based ecosystems but also to audit the security of on-chain applications. However, exploring this blockchain “big data” is challenging due to data heterogeneity and the huge amount. Existing blockchain exploration techniques are either incomplete or inefficient, making them inapt in time-sensitive applications. This paper presents <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf BlockExplorer}$</tex-math></inline-formula> , an efficient and flexible blockchain exploration system for Ethereum. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf BlockExplorer}$</tex-math></inline-formula> builds on a master-slave architecture, where the master partitions all blocks into multiple non-overlapped sets and each slave simultaneously processes Ethereum big data based on a set of blocks. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf BlockExplorer}$</tex-math></inline-formula> implements a transaction-based partitioning approach to address load balance among slaves, and a code instrumentation approach to acquire complete Ethereum big data. The evaluation shows that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf BlockExplorer}$</tex-math></inline-formula> accelerates the data acquisition performance of the state-of-the-art by 4.1×, while the workload difference among slaves is up to 18%. To demonstrate the application of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf BlockExplorer}$</tex-math></inline-formula> , we develop three apps upon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf BlockExplorer}$</tex-math></inline-formula> to detect real-life attacks against Ethereum and show that our apps can detect attacks in a large range of blocks (e.g., ten million) within a short time (e.g., multiple hours).
Blockchain technology provides a data structure with inherent security properties that include cryptography, decentralization, and consensus, which ensure trust in transactions. It covers widely applicable usages, such as in intelligent manufacturing, finance, the Internet of things (IoT), medicine and health, and many different areas, especially in medical health data security and privacy protection areas. Its natural attributes, such as contracts and consensus mechanisms, have leading-edge advantages in protecting data confidentiality, integrity, and availability. The security issues are gradually revealed with in-depth research and vigorous development. Unlike traditional paper storage methods, modern medical records are stored electronically. Blockchain technology provided a decentralized solution to the trust-less issues between distrusting parties without third-party guarantees, but the “trust-less” security through technology was easily misunderstood and hindered the security differences between public and private blockchains appropriately. The mentioned advantages and disadvantages motivated us to provide an advancement and comprehensive study regarding the applicability of blockchain technology. This paper focuses on the healthcare security issues in blockchain and sorts out the security risks in six layers of blockchain technology by comparing and analyzing existing security measures. It also explores and defines the different security attacks and challenges when applying blockchain technology, which promotes theoretical research and robust security protocol development in the current and future distributed work environment.
The research topics of this paper are the data security of the edge devices and terminals of the Internet of Things (IoT) and the consensus design of a lightweight blockchain for the Internet of Things. These devices have self-organization capabilities to overcome the bandwidth delay and service-congestion problems caused by excessive concentration in existing scenarios, but they face the challenges of limited computing, storage, and communication resources. As a result, a non- financial lightweight blockchain consensus design with low energy consumption, low latency, and greater stability should be investigated. We propose a hierarchical proof-of-capability (HPoC) consensus mechanism combined with the asynchronous proof-of-work (PoW) mechanism for improving the computing capacity, storage capacity, and communication capacity of IoT edge devices that can generate blocks with low latency, low power consumption, and strong stability in resource-constrained edge device nodes, while ensuring that the security of the edge devices is enhanced asynchronously. We simulated a smart-home scenario, with the number of device nodes ranging from 15 to 75, and conducted comparative experiments between HPoC and PoW based on different difficulty bits. The experimental results showed that HPoC is a consensus mechanism with scalability and stability that can flexibly adjust time consumption and accurately select nodes with strong capabilities to generate blocks in heterogeneous devices.
The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest–Shamir–Adleman (RP2-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP2-RSA attains a 96.123% security level.
Daniel Soesanto, Teguh Bharata Adji, Igi Ardiyanto
Several studies have been carried out to improve the blockchain’s Proof of Work (PoW) consensus method because it weakens its consensus latency. This is in addition to the introduction of consensus mechanisms, ranging from Proof of Stake (PoS) to Flexible Proof of Activity (PoA). However, these mechanisms are unable to increase latency significantly. In this study, an adaptive PoW framework design was developed by applying multiple mempool, which impact differences in the transaction speed of each transaction. The proposed method can facilitate different levels of consensus difficulty in each transaction. The difficulty level adjustment in PoW is based on collected and processed transaction data to obtain the level of importance in each transaction. The difficulty level in baseline PoW is used as the maximum level of the developed adaptive process, followed by their definition. Finally, higher difficulty levels are assigned to more critical transactions and vice versa. Therefore, the latency will increase as the consensus difficulty level rises and vice versa. Initial simulation results showed that when tested with 6,000 transaction data, the adaptive PoW framework design can increase the average number of transactions per second by up to 400%.
Proof-of-Work (PoW) was the first blockchain consensus protocol discovered, followed by Proof-of-Stake and others. The disadvantage of the PoW is that it requires high energy consumption compared to other consensus protocols. Based on this weakness, some researchers proposed a lightweight blockchain technology, a modified blockchain that has a simplified algorithm but does not reduce the security factor. This lightweight blockchain is suitable for applications requiring data reliability but with limited computing resources, such as Internet of Things devices. This paper discussed and modified the SimBlock simulator as one of the existing blockchain simulators. It has a visualization tool to look further into the propagation transition of the block. Unfortunately, the existing PoW consensus on the SimBlock simulator is unable to pinpoint the actual hash computation method. Therefore, the hashing process in the SimBlock simulator was modified by including the difficulty level for finding the hash target. The purpose of including the difficulty level was to determine how long it takes to create a block. By knowing the time needed, a recommendation could be obtained for the most suitable difficulty level for a lightweight blockchain and its implementation with IoT devices. There are two options of approaches to the difficulty level referred to in this paper; finding the number of zeros that appear sequentially and are in front of a hash value (leading zero) and finding the number of zeros that appear arbitrarily (count zero). For example, the first difficulty level on a leading-zero quest has the same meaning as searching for a leading zero, the second level of difficulty is the search for the two leading zeros, etc. The block generation time on a blockchain network using the PoW consensus highly depends on the difficulty level. Block generation time and resource utility have been analyzed and compared with other blockchain simulators and existing networks, such as Ethereum and Bitcoin. The modified SimBlock simulator was tested in this experiment using 100-600 nodes, with the expected result of creating 100-1000 blocks. Based on the experiments, creating a block using leading zeros as the hash target for the first to fourth difficulty levels took less than 1 s, whereas when using count zeros (zero-count) as the target hash, it took less than 1 s for the first to fifteenth difficulty levels. Using leading zeros took approximately 237.4 s at difficulty level 7, while count-zero took approximately 633.8 s at difficulty level 19. The experiment was not continued at the next difficulty level because it required a longer compilation time. With the modifications made, the creation of a block on a blockchain network using the PoW consensus can be clearly seen. The difficulty level added to the simulator can also provide information for determining the difficulty level to be implemented on the lightweight blockchain.
Classifying brain signals collected by wearable Internet of Things (IoT) sensors, especially brain-computer interfaces (BCIs), is one of the fastest-growing areas of research. However, research has mostly ignored the secure storage and privacy protection issues of collected personal neurophysiological data. Therefore, in this article, we try to bridge this gap and propose a secure privacy-preserving protocol for implementing BCI applications. We first transformed brain signals into images and used generative adversarial network to generate synthetic signals to protect data privacy. Subsequently, we applied the paradigm of transfer learning for signal classification. The proposed method was evaluated by a case study and results indicate that real electroencephalogram data augmented with artificially generated samples provide superior classification performance. In addition, we proposed a blockchain-based scheme and developed a prototype on Ethereum, which aims to make storing, querying and sharing personal neurophysiological data and analysis reports secure and privacy-aware. The rights of three main transaction bodies - construction workers, BCI service providers and project managers - are described and the advantages of the proposed system are discussed. We believe this paper provides a well-rounded solution to safeguard private data against cyber-attacks, level the playing field for BCI application developers, and to the end improve professional well-being in the industry.
Blockchain technology is becoming a promising technological solution for enterprise applications with the rise of interoperable solutions. A cross-chain architecture facilitates interoperability, thus improves its chain efficiency, reduces fragmentation, and allows users and features to flow more freely across multiple blockchains. However, enabling interoperability in silo networks will make a significant functional trade-off on the security and performance of the system. This paper review trade-offs in blockchain technologies related to interoperability.
Chenyuan Wu, B.R. Mehta, Mohammad Javad Amiri, Ryan Marcus · 5 authors
This paper presents AdaChain, a learning-based blockchain framework that adaptively chooses the best permissioned blockchain architecture in order to optimize effective throughput for dynamic transaction workloads. AdaChain addresses the challenge in the Blockchain-as-a-Service (BaaS) environments, where a large variety of possible smart contracts are deployed with different workload characteristics. AdaChain supports automatically adapting to an underlying, dynamically changing workload through the use of reinforcement learning. When a promising architecture is identified, AdaChain switches from the current architecture to the promising one at runtime in a way that respects correctness and security concerns. Experimentally, we show that AdaChain can converge quickly to optimal architectures under changing workloads, significantly outperform fixed architectures in terms of the number of successfully committed transactions, all while incurring low additional overhead.
Anupama Ray, Sai Sakunthala Guddanti, Vishnu Ajith, Dhinakaran Vinayagamurthy
Ethereum is one of the most valuable blockchain networks in terms of the total monetary value locked in it, and arguably been the most active network where new blockchain innovations in research and applications are demonstrated. But, this also leads to Ethereum network being susceptible to a wide variety of threats and attacks in an attempt to gain unreasonable advantage or to undermine the value of the users. Even with the state-of-art classical ML algorithms, detecting such attacks is still hard. This motivated us to build a hybrid system of quantum-classical algorithms that improves phishing detection in financial transaction networks. This paper presents a classical ensemble pipeline of classical and quantum algorithms and a detailed study benchmarking existing Quantum Machine Learning algorithms such as Quantum Support Vector Machine and Variational Quantum Classifier. With the current generation of quantum hardware available, smaller datasets are more suited to the QML models and most research restricts to hundreds of samples. However, we experimented on different data sizes and report results with a test data of 12K transaction nodes, which is to the best of the authors knowledge the largest QML experiment run so far on any real quantum hardware. The classical ensembles of quantum-classical models improved the macro F-score and phishing F-score. One key observation is QSVM constantly gives lower false positives, thereby higher precision compared with any other classical or quantum network, which is always preferred for any anomaly detection problem. This is true for QSVMs when used individually or via bagging of same models or in combination with other classical/quantum models making it the most advantageous quantum algorithm so far. The proposed ensemble framework is generic and can be applied for any classification task
Telemedicine has the potential to be a good resource for early disease diagnosis, provided that it is utilised in the correct manner. The Internet of Things (IoT) is a concept that has developed in recent years as people have become more aware that they are continuously being watched. As a result of the increased prevalence of neurodegenerative disorders like Alzheimer's disease (AD), biomarkers for these conditions are in high demand for early-stage resource prognosis. Because of the precarious nature of the situation, it is absolutely necessary for these structures to offer remarkable qualities such as accessibility and precision. Deep learning strategies could be useful in fitness applications in situations in which there are a large number of data points to be analysed. Excellent data for a decentralized Internet of Things device that is based on block chain technology. By utilizing a connection to the internet that is of a high speed, it is feasible to obtain a prompt answer from these structures. It is not possible to run deep learning algorithms on smart gateway devices since they do not have sufficient computational capacity. In this study, we investigate the potential for increasing the speed of data flow in the healthcare industry while simultaneously improving data quality through the incorporation of blockchain-based deep neural networks into the control system. Experiments are being conducted to evaluate the speed and accuracy of real-time fitness tracking for the purpose of classifying groups. We are able to determine if diseases of the brain are benign or malignant by employing a model that utilises deep learning. For the purpose of determining the relative severity of each condition, the research examines the symptoms of several different mental diseases and compares them to those of Alzheimer's disease, moderate cognitive impairment, and normal cognition. The research calls for a number of different procedures. The majority of the data is used to train the classifiers, while the remainder of the data is utilised in conjunction with an ensemble model and meta classifier to classify individuals into the appropriate categories. The OASIS-three database is a long-term study that incorporates neuroimaging, cognitive, clinical, and biomarker measurements. This study focuses on healthy ageing as well as Alzheimer's disease. When comparing the outcomes of the simulation to those acquired from the real world, the OASIS-three database (AD), in addition to the ADNI UDS dataset, is employed as a comparison tool. The findings show that answers to questions about this issue can be arrived at quickly and categorized utilizing an in-depth methodology (98% accuracy).
Traditional centralized electromagnetic spectrum monitoring platforms collect energy detection data from time, frequency and space dimensions. This method has high data redundancy. Combining the propagation loss characteristics and the signal direction finding (DF) data of each detection node, we focus on the signal source compressed parameter estimation. We propose a minimum average distance (MAD) method to improve the accuracy of collaborative detection in Cognitive Radio Network (CRN). The collaborative estimated data is stored in the blockchain structure to establish the distributed electromagnetic spectrum database (BC-DSDB). Based on the consensus mechanism Proof of High Confidence (POHC), the detection nodes maintain BC-DSDB independently. To regulate the rational utilization of electromagnetic spectrum resources, we propose the Spectrum Resource Currency (SRC) to evaluate the priority of the secondary user (SU) for dynamic spectrum access. When a spectrum collision event occurs between SUs, the spectrum time slice resources can be allocated according to the SRC. The experimental results show that BC-DSDB accurately describes the distribution of electromagnetic spectrum resources based on the propagation loss characteristics. At the same time, the redundancy of spectral data storage is reduced. SUs can quickly formulate dynamic spectrum access policies based on BC-DSDB and SRC in distributed cognitive radio networks.
In a Proof of Stake (PoS) blockchain, all nodes need to be able to recognize approximately synchronized clocks. A PoS blockchain relies on external clocks, and each node synchronizes its local clock according to an external clock using Network Time Protocol (NTP) or a similar protocol. However, this external dependence is undesirable because it reduces the autonomy, sustainability and trustless nature of the blockchain. This paper presents a clock synchronization protocol for slot-based PoS blockchains. The proposed protocol estimates the block propagation time to ensure that the synchronization is closer to that of a real-world clock. In addition, the proposed method considers the clock drift and clock adjustment frequency to achieve more accurate synchronization. Simulation experiments show that the proposed method can synchronize with a time that is closer to that of a real-world clock and with higher accuracy than existing methods.
Speech is one form of biometric that combines both physiological and behavioral features. It is beneficial for remote-access transactions over telecommunication networks. Presently, this task is the most challenging one for researchers. People's mental status in the form of emotions is quite complex, and its complexity depends upon internal behavior. Emotion and facial behavior are essential characteristics through which human internal thought can be predicted. Speech is one of the mechanisms through which human's various internal reflections can be expected and extracted by focusing on the vocal track, the flow of voice, voice frequency, etc. Human voice specimens of different ages can be emotions that can be predicted through a deep learning approach using feature removal behavior prediction that will help build a step intelligent healthcare system strong and provide data to various doctors of medical institutes and hospitals to understand the physiological behavior of humans. Healthcare is a clinical area with data concentrated where many details are accessed, generated, and circulated periodically. Healthcare systems with many existing approaches like tracing and tracking continuously disclose the system's constraints in controlling patient data privacy and security. In the healthcare system, majority of the work involves swapping or using decisively confidential and personal data. A key issue is the modeling of approaches that guarantee the value of health-related data while protecting privacy and observing high behavioral standards. This will encourage large-scale perception, especially as healthcare information collection is expected to continue far off this current ongoing pandemic. So, the research section is looking for a privacy-preserving, secure, and sustainable system by using a technology called Blockchain. Data related to healthcare and distribution among institutions is a very challenging task. Storage of facts in the centralized form is a targeted choice for cyber hackers and initiates an accordant sight of patients' facts which will cause a problem in sharing information over a network. So, this research paper's approach based on Blockchain for sharing sufferer data in a secured manner is presented. Finally, the proposed model for extracting optimum value in error rate and accuracy was analyzed using different feature removal approaches to determine which feature removal performs better with different voice specimen variations. The proposed method increases the rate of correct evidence collection and minimizes the loss and authentication issues and using feature extraction based on text validation increases the sustainability of the healthcare system.
With the evolution of information technology, the use of internet of things has increased. It affects several areas such as medical field, smart cities, and information systems. In this work, we will use this technological development in the context of health, particularly e-health. We present a platform based on IoMT to allow the monitoring of patient’s health. To meet the constraint of medical secrecy and confidentiality of information, we will use the Blockchain as a secure system. Our system will use the data collected by several smart sensors such as blood pressure, SPO2 concentration, and EEG signals. These encrypted data will be collected by an embedded Raspberry PI 4 platform (working as a smart data relay) before being processed (on a backend server) and then saved in an embedded Blockchain node. The preliminary results show the effectiveness of the proposed platform as a candidate of a low-cost example of secured Electronic Health Record (EHR).
Giulia Rafaiani, Paolo Santini, Marco Baldi, Franco Chiaraluce
The growing interest in Internet of Things (IoT) and Industrial IoT (IIoT) poses the challenge of finding robust solutions for the certification and notarization of data produced and collected by embedded devices. The blockchain and distributed ledger technologies represent a promising solution to address these issues, but rise other questions, for example regarding their practical feasibility. In fact, IoT devices have limited resources and, consequently, may not be able to easily perform all the operations required to participate in a blockchain. In this paper we propose a minimal architecture to allow IoT devices performing data certification and notarization on the Ethereum blockchain. We develop a hardware-software platform through which a lightweight device (e.g., an IoT sensor), holding a secret key and the associated public address, produces signed transactions, which are then submitted to the blockchain network. This guarantees data integrity and authenticity and, on the other hand, minimizes the computational burden on the lightweight device. To show the practicality of the proposed approach, we report and discuss the results of benchmarks performed on ARM Cortex-M4 hardware architectures, sending transactions over the Ropsten testnet. Our results show that all the necessary operations can be performed with small latency, thus proving that an IoT device can directly interact with the blockchain, without apparent bottlenecks.
Our brain is made of thousands of neurons that create a chain of different episodes stored in the human brain in a decentralized manner that shapes human conscious experience just like blockchain technology. Although the human brain can voluntarily delete some episodes stored by the mechanism of distributed neurons, this ability is limited. Thus, each episode is open to access and saved securely so that we do not confuse our childhood memories with movie characters same as blocks in a decentralized ledger (Allegri, 2021). In this paper, it is investigated that some properties of the human brain can be to some extent analogical to blockchain and that certain blockchain concepts can be applied to the development of neuroscience as knowledge of the workings of the human brain and memories. At the same time, the latest advancements in blockchain technology can be fruitful in the development of neuroscience. To closely examine the correlation between neuroscience and blockchain, decentralized ledger technology concepts and neuroscience basics are presented [1].