Rana Fareed Ghani, Asia A. Salman, Abdullah B. Khudhair, Laith Al-Jobouri
One of the major capabilities of blockchain technology is the sharing of data in verifiable ways without losing control of information possession. Issuing and verifying student certifications for higher study applications or job recruitment require many steps that take days to complete and are considered time-consuming. Most universities around the world use centralized systems to control the entire procedure when a graduate applies for a job or postgraduate studies. Applying blockchain technology to certificate verification protocols through a comprehensive architecture provides authenticity and reduces time significantly. In this paper, a framework has been proposed to issue student certifications locally in addition to sharing them across the internet while maintaining control and ownership of the certifications. This framework leverages the advantages of blockchain technology to electronic certification sharing and verification. Applying the proposed blockchain-based certification system in universities will provide low latency for issuing, sharing, and verification of these certifications. The paper presents the proposed blockchain-based framework for e-certification sharing and an evaluation of the framework, which consists of measuring the average time to issue a certificate and transaction latency time.
Kai Chen, Cheng Xu, Hongzhe Liu, Pengfei Wang · 5 authors
The development of 5G network communication has brought technological innovation to smart city communication, making the realization of V2X (vehicle to everything) technology possible. Vehicles wirelessly communicate with other vehicles, sensors, pedestrians, and roadside units, raising data security issues while driving. In order to ensure driving safety, the risk map cognitive model is established with the help of blockchain technology. In this model, the key map data and personal privacy information are encrypted and uploaded to form a blockchain, and the smart contract technology is used for automatic script processing. Then, according to different risk scenarios, cognitive learning is carried out for different risk levels, the cognitive results and corresponding operations are fed back to the intelligent vehicle, and these operations ensure the safe operation of the vehicle according to the intelligent vehicle. Finally, the feasibility of the model was verified by comparing different dangerous scenarios. The experimental results show that this risk cognition model can cognize the data of the intelligent vehicle according to different danger scenarios, and the model can transmit acceleration, deceleration, braking, and other behaviors to the intelligent vehicle to ensure smart city driving safety.
Zhongxing Ming, Mingzhao Zhou, Laizhong Cui, Shu Yang
The Internet of Medical Things is developing rapidly in recent years. However, the timeliness and security of healthcare applications challenge its adoption. In this article, we propose a blockchain-assisted edge computing platform that timely and securely processes time-sensitive healthcare applications. We propose a blockchain-assisted framework that leverages distributed edge servers to achieve fast data processing. We design smart contracts to verify the identity and data credibility of network entities. We formulate the problem as a directed acyclic graph organized scheduling model and develop online orchestrating algorithms to meet the timeliness requirement. We implement the blockchain prototype and evaluate the performance of the proposed algorithm under extensive configurations. Results show that fast blockchain-assisted edge computing platform for healthcare achieves a significant timeliness guarantee, and at the same time outperforms conventional schemes from the latency perspective.
Due to recent development in technology, major changes have been noticed in human being’s life. Today’s lives of human being are becoming more convenient (i.e., in terms of living standard). In current real-world’s applications, we have shifted our attention from wired devices to wireless devices. In result, we moved into the era of smart technology, where many internet devices are interconnected in a distributed and decentralized way. Such internet connected devices (ICDs) or internet of things (IoTs) are generating a lot of data (i.e., via communicating other smart devices). With the tremendous increase in the amount of data, there is a higher requirement to process this huge amount of data (generated through billion of ICDs) using efficient ML algorithms. In the past decade, we refer data mining (DM) algorithms to make some decision from collected data-sets. But, due to increasing data on a large scale, DM fail to handle this data. So, as substitute of DM algorithms and to refine this information in an efficient manner, we require tradition analytics algorithms, i.e., ML or DM algorithms. In current scenario, some of the ML algorithms (available to analysis this data) are supervised (used with labeled data), unsupervised (used with unlabeled data) and semi-supervised (work as reward-based learning). Supervised learning algorithms are like Linear Regression, Classification, and k-nearest neighbor 150(KNN), etc. Whereas, unsupervised learning algorithms are clustering, k-means, etc. In general, ML focuses on building the systems that learn and hence improves with the knowledge and experience. Being the heart of artificial intelligence (AI) and data science, ML is gaining popularity day by day. Notice that a sub-set of AI is ML. Several algorithms have already been developed (in the past decade) for processing of data, although this field focuses on developing new learning algorithm for big data computability with minimum complexity (i.e., in terms of time and space). ML algorithms are not only applicable to computer science field but also extend to medical, psychological, marketing, manufacturing, automobile, etc. On another side, Big Data including Deep learning are the two primary and highly demandable fields of data science. Here, Deep learning is a subset of ML, also a part of computer vision or AI. The large (or massive) amount of data related to a specific domain which forms Big Data (in form of 5 Vs like Velocity, Volume, Value, Variety, and Veracity), contains valuable information related to various fields like marketing, automobile, finance, cyber security, medical, fraud detection, etc. Such real-world’s applications are creating a lot of information every day. The valuable (i.e., needful, or meaningful) information required to be processed (or retrieved) from analysis of this unstructured/large amount of data for further processing of the data for future use (or for prediction). Big organizations have to deal with the large amount of data for prediction, classification, decision making, etc. The use of ML algorithms for big data analytics (DA) includes deep learning, which extracts the high-level semantics from the valuable (meaningful) information form the data. It uses hierarchical process for efficient processing and retrieving the complex abstraction from the data. Hence, this chapter discusses several algorithms of ML, to analysis Big Data. The AI subset, including ML algorithms, is also, Deep learning algorithms is being discussed here (i.e., analyzing this Big Data for accurate prediction). Later, this chapter focuses on the benefits of ML, deep learning algorithms in analyzing the large amount of data (i.e., in unsupervised or unstructured form) for numerous complex problems like information retrieval, medical diagnosis, cognitive science, indexing using semantic analysis, data tagging, speech recognition, natural language processing (NLP), etc. Also, weakness, raised issues, and challenges (during analysis big data) using (in) ML or deep learning have been discussed in detail. In other words, research gaps in using ML, deep learning algorithms for big data will also be discussed (with covering future research directions/trends). In last, the importance of smart era, computational intelligence, AI has been discussed in this chapter in detail.
Generally, image protection is often accomplished by digital watermark, which is also widely used in the field of data protection and data ownership authentication. However, the existing digital watermark has inherent disadvantages, it needs completely trusted third parties to act as the arbitration parties, which may be difficult to achieve. Second, an image watermark algorithm will inevitably lead to the loss of image data when operating on the image data, while it is usually irreversible. The zero-watermark algorithm can solve the problem of data loss but relies more on the trusted third party than a traditional digital watermark, which makes its prospects limited. With the development of blockchain technology in recent years, its features of de-trusted third parties combine the fairness and process automation of smart contracts to make up for the shortcomings of the zero-watermarking algorithm. This paper studies image storage and authentication frameworks. This framework utilizes the advantages of zero-watermarking algorithm and blockchain, using the Inter-Planetary File System to solve the data expansion problem of blockchain, which well avoids the shortcomings of zero-watermarking algorithm and blockchain. Experiment and inference illustrate that the proposed framework is feasible.
Advanced Steganography and Watermarking Techniques
In recent years, as the blockchain and the cryptocurrency built on it have become popular, a large number of decentralized financial applications have been built on the Ethereum network. This makes the security issues on Ethereum attract more and more researchers' attention. Phishing scams on Ethereum have caused people to suffer huge economic losses. Recently, with the popularity of graph convolutional neural networks (GCN), many models based on GCN for node classification have emerged. However, these current GCN models are difficult to cope with the challenges caused by the lack of side information and labels of nodes in the Ethereum network. In this paper, we propose a semisupervised graph convolutional neural network model based on important neighbors for the identification of phishing scam nodes on Ethereum. In our work, we design the pretext task for the node embedding module so that our model can learn the appropriate node embedding by using a large amount of unlabeled node data. Subsequent experiments show that our proposed model is better than all other baselines, which proves the effectiveness of our model.
Feng Liu, Chengyi Yang, Jie Yang, Deli Kong · 7 authors
As a distributed storage scheme, the blockchain network lacks storage space has been a long-term concern in this field. At present, there are relatively few research on algorithms and protocols to reduce the storage requirement of blockchain, and the existing research has limitations such as sacrificing fault tolerance performance and raising time cost, which need to be further improved. Facing the above problems, this paper proposes a protocol based on Distributed Image Storage Protocol (DISP), which can effectively improve blockchain storage space and reduces computational costs in the help of InterPlanetary File System (IPFS). In order to prove the feasibility of the protocol, we make full use of IPFS and distributed database to design a simulation experiment for blockchain. Through distributed pooling (DP) algorithm in this protocol, we can divide image evidence into recognizable several small files and stored in several nodes. And these files can be restored to lossless original documents again by inverse distributed pooling (IDP) algorithm after authorization. These advantages in performance create conditions for large scale industrial and commercial applications.
Sana Amjad, Shahid Abbas, Zain Abubaker, Mohammed H. Alsharif · 6 authors
This paper proposes a blockchain-based node authentication model for the Internet of sensor things (IoST). The nodes in the network are authenticated based on their credentials to make the network free from malicious nodes. In IoST, sensor nodes gather the information from the environment and send it to the cluster heads (CHs) for additional processing. CHs aggregate the sensed information. Therefore, their energy rapidly depletes due to extra workload. To solve this issue, we proposed distance, degree, and residual energy-based low-energy adaptive clustering hierarchy (DDR-LEACH) protocol. DDR-LEACH is used to replace CHs with the ordinary nodes based on maximum residual energy, degree, and minimum distance from BS. Furthermore, storing a huge amount of data in the blockchain is very costly. To tackle this issue, an external data storage, named as interplanetary file system (IPFS), is used. Furthermore, for ensuring data security in IPFS, AES 128-bit is used, which performs better than the existing encryption schemes. Moreover, a huge computational cost is required using a proof of work consensus mechanism to validate transactions. To solve this issue, proof of authority (PoA) consensus mechanism is used in the proposed model. The simulation results are carried out, which show the efficiency and effectiveness of the proposed system model. The DDR-LEACH is compared with LEACH and the simulation results show that DDR-LEACH outperforms LEACH in terms of energy consumption, throughput, and improvement in network lifetime with CH selection mechanism. Moreover, transaction cost is computed, which is reduced by PoA during data storage on IPFS and service provisioning. Furthermore, the time is calculated in the comparison of AES 128-bit scheme with existing scheme. The formal security analysis is performed to check the effectiveness of smart contract against attacks. Additionally, two different attacks, MITM and Sybil, are induced in our system to show our system model's resilience against cyber attacks.
Chenchen Han, Gwang-Jun Kim Chenchen Han, Osama Alfarraj Gwang-Jun Kim, Amr Tolba Osama Alfarraj · 5 authors
<p>With the rapid development of 6G communication technology, data security of the Internet of Things (IoT) has become a key challenge. This paper first analyzes the security issues and risks of IoT data storage in 6G, and then constructs a blockchain-based zero-trust data storage scheme (ZT-BDS) in 6G edge IoT to ensure data security. Under this framework, an improved scratch-off puzzle based on Proof of Recoverability (PoR) is firstly constructed to realize distributed IoT data storage, which can reduce resource consumption compared with other existing schemes. Secondly, the accumulator is used to replace the Merkle trees to store IoT data in the blockchain. Since the accumulator can provide not only membership proof, but also non-membership proof, the proposed blockchain-based data storage scheme is more secure. Thirdly, PoW is replaced by an improved PoR scheme as the consensus protocol. On the one hand, PoR can verify the integrity of data, which will further enhance the security of IoT data; on the other hand, the proposed PoR is composed of polynomial commitment, which can reduce bandwidth with the aid of the aggregation function of polynomial commitment. Experimental comparisons show that our scheme has better bandwidth and storage capacity.</p> <p>&nbsp;</p>
The purposes are to develop the patent data profoundly, control the data access process effectively, and protect the patent information and content. The traditional patent review systems are analyzed. For the present patent data security and privacy protection technologies and algorithms, the patent information data are stored on different block nodes after data fragmentation using blockchain technology. Then the data are shared using the data encryption algorism. In this way, data access control can be restricted to particular users. Finally, a patent data protection scheme based on privacy protection is proposed. The security of the scheme and the model performance are verified through simulation experiments. The time required to encrypt 10 MB files with 64-bit and 128-bit data is 35 ms and 105 ms, respectively. The proposed re-encryption algorithm only needs 1 s to decrypt 64 KB data, and only 1% of the data needs asymmetric encryption. This greatly reduces the computational overhead of encryption. Results demonstrate that the system can effectively control the access methods of users, efficiently protect the personal privacy and patent content of patent applicants, and reduce the patent office cloud computing overhead using the local resources of branches. The distributed storage methods can reduce the cloud system interaction of the patent office, thereby greatly improving the speed of encryption and ensuring data security. Compared with the state of the art methods, the proposed patent data access and protection system based on blockchain technology have greater advantages in data security and model performance. The research results can provide a research foundation and practical value for the protection and review systems of patent data.
Blockchain is also known as distributed ledger. All full nodes connected to the blockchain network participate in the maintenance of the data in the ledger. It is a technology in many fields such as computer science, cryptography, distributed storage, and finance. Industrial and academic research on blockchain technology has achieved great results, including research on blockchain networks, consensus mechanisms, and smart contracts. However, limited by the data storage mode and the characteristics of distributed storage at the bottom of the blockchain, there are still problems that need to be solved urgently, such as the single retrieval function and the low retrieval rate of the data retrieval on the blockchain. We focused on this problem, and based on the built-in index and external data warehouse method, we proposed a high-speed data retrieval model on blockchain. The model consists of three parts: a blockchain network with improved index storage, a data processing cluster, and application layer services. The new blockchain network improves the organization of transaction data in the traditional blockchain system, and designs a data structure suitable for high-speed retrieval to organize transaction data; the data processing cluster is responsible for ensuring data consistency and in accordance with high efficiency The synchronization strategy is to synchronize the data on the chain to the relational data warehouse under the chain; the application layer service encapsulates the rich query functions supported by the relational database, and finally provides services to the outside in the form of HTTP, RPC, etc. Experimental results show that the model can effectively expand the blockchain system in terms of query efficiency and query functions, improve the query rate of data on the blockchain, and meet people's needs for blockchain query functions.
S. Neelakandan, J. Rene Beulah, L. Prathiba, G. L. N. Murthy · 6 authors
At these times, internet of things (IoT) technologies have become ubiquitous in the healthcare sector. Because of the increasing needs of IoT, massive quantity of patient data is being gathered and is utilized for diagnostic purposes. The recent developments of artificial intelligence (AI) and deep learning (DL) models are commonly employed to accurately identify the diseases in real-time scenarios. Despite the benefits, security, energy constraining, insufficient training data are the major issues which need to be resolved in the IoT enabled medical field. To accomplish the security, blockchain technology is recently developed which is a decentralized architecture that is widely utilized. With this motivation, this paper introduces a new blockchain with DL enabled secure medical data transmission and diagnosis (BDL-SMDTD) model. The goal of the BDL-SMDTD model is to securely transmit the medical images and diagnose the disease with maximum detection rate. The BDL-SMDTD model incorporates different stages of operations such as image acquisition, encryption, blockchain, and diagnostic process. Primarily, moth flame optimization (MFO) with elliptic curve cryptography (ECC), called MFO-ECC technique is used for the image encryption process where the optimal keys of ECC are generated using MFO algorithm. Besides, blockchain technology is utilized to store the encrypted images. Then, the diagnostic process involves histogram-based segmentation, Inception with ResNet-v2-based feature extraction, and support vector machine (SVM)-based classification. The experimental performance of the presented BDL-SMDTD technique has been validated using benchmark medical images and the resultant values highlighted the improved performance of the BDL-SMDTD technique. The proposed BDL-SMDTD model accomplished maximum classification performance with sensitivity of 96.94%, specificity of 98.36%, and accuracy of 95.29%, whereas the feature extraction is performed based on ResNet-v2
Blockchain Technology Applications and Security
Brain Tumor Detection and Classification
Advanced Steganography and Watermarking Techniques
In the last few years, machine learning (ML) and blockchain are the most prominent innovations. Blockchain’s potential has been widely explored in literature and media, especially in finance and payment industries. Data confidentiality and privacy are prioritized in blockchain’s decentralized database. However, this procedure is time consuming and inconvenient, which is one of the explanations why blockchain technology has yet to gain widespread acceptance. To solve the invalid dataset, we used integrated blockchain and ML approaches to secure system transactions and manage a dataset. Mostly, blockchain can greatly facilitate the exchange of training data and ML models, as well as decentralized information, stability, anonymity, and trustworthy ML decision making. We study the literature on integrating blockchain and ML systems in this paper and show how they can work together efficiently and effectively. We will go through the problems that each industry faces when it comes to implementing blockchain. We present a systematic report on ML and blockchain-based smart Industry 4.0 applications more robust to attacks in this article. Finally, we suggest some potential research avenues and anticipate further studies into the deeper convergence of the two promising technologies. We hope that our results will help decision-makers embrace blockchain technology and invest in Industry 4.0 by empowering and promoting research in this field.
Healthcare transformation is becoming one of the highest priorities in a world whereby remarkable advances in technology are taking place. Recent healthcare data fusion management systems are centralized, which possess the probability of failure in case of a natural disaster. Blockchain has expanded fast to be the most widely spoken innovation that could address a large number of present data management problems in the health care sector. The usage of blockchain technology for the distribution of secure and safe health care datasets has received all the attention. This article presents a Bat Optimization Algorithm with Fuzzy Neural Network Based Classification (BOA-FNNC) Model for Blockchain Assisted Healthcare Data Fusion Environment. The presented BOA-FNNC technique mainly focuses on achieving security in the healthcare sector using BC technology. For accomplishing this, the BOA-FNNC technique performs BC assisted data transmission in the medical sector. Besides, the VGG-16 model is exploited for the creation of feature vectors. To classify healthcare data, the BOA with FNN model is utilized in this study, where the BOA fine tune the parameters related to the FNN model which in turn boosts the classifier efficiency. For illustrating the betterment of the BOA-FNNC technique, a series of experiments were performed. The comparison study reported the enhancements of the BOA-FNNC technique over other recent approaches.
With the increasing number of coronavirus disease 2019 cases and worldwide vaccination coverage, 'vaccination passports' (vaccination certificates) may become a required permit for global travel, thereby supporting economic recovery. On March 7, 2021, Wang Yi, State Councilor and Foreign Minister of China, announced the launch of the Chinese version of an 'international travel health certificate' at a press conference of the National People's Congress and the Chinese Political Consultative Conference. He proposed a feasible 'Chinese solution' for promoting the recovery of the global economy and the facilitation of cross-border travel, and hoped that the international travel health certificate and vaccination passport can be mutually authenticated. The Israeli government issued a
Open access
Artificial Intelligence in Healthcare and Education
E. Suresh Babu, Madhura S. Rao, Satuluri Naganjaneyulu, M. Srinivasa Sesha Sai · 5 authors
This paper provides a solution to the educational certification issue by employing the blockchain network. The proposed permissioned blockchain network is implemented in hyperledger fabric. The proposed system provides various services to issuing institutions: verifying organisation; identification and authentication of the issuer; verify and securely share academic records to the recipients; and stores the academic records in the blockchain in a distributed manner, ensuring the privacy of stored records of the recipient. When compared to Ethereum, hyperledger fabric provides additional functionalities like efficient parallelism, concurrency, multiple transaction executions, and efficient commitments of the transaction into the ledger. The experimental analysis of the proposed system has been executed to test the performance of invoking and query transactions using Hyperledger Caliper. We analyse the throughput and transaction latency of the proposed work as well. The experimental results exhibit the proposed system achieves better transaction processing power and security compared to existing systems.
Mohammad Alamgeer, Saud S. Alotaibi, Shaha Al‐Otaibi, Nazik Alturki · 8 authors
Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge technologies. Blockchain, data encryption, and deep learning (DL) models can be utilized to design efficient security solutions for IoT healthcare applications. In this aspect, this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning (BPPIE-ODL) technique for IoT healthcare applications. The proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud server. The proposed BPPIE-ODL technique encompasses the design of dragonfly algorithm (DFA) with signcryption technique to encrypt the medical images captured by the IoT devices. Besides, blockchain (BC) can be utilized as a distributed data saving approach for generating a ledger, which permits access to the users and prevents third party’s access to encrypted data. In addition, the classification process includes SqueezeNet based feature extraction, softmax classifier (SMC), and Nadam based hyperparameter optimizer. The usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet architecture. For examining the enhanced encryption as well as classification performance of the BPPIE-ODL technique, a comprehensive experimental analysis is carried out. The simulation outcomes demonstrate the significant performance of the BPPIE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Blockchain technology is widely used in finance, supply chain, Internet of Things and other fields because of its advantages of anti-tampering, decentralization, and traceability. As the core factor affecting the performance of blockchain, consensus algorithm with good performance is the current research focus and goal. Aiming at the problem of insufficient performance and scalability of Practical Byzantine Fault Tolerance (PBFT), a two-stage verification algorithm is proposed. The algorithm improves the three-stage verification of PBFT into the confirmation stage and the review stage. The block contains the confirmation information of the previous block, and the block release and information confirmation are carried out synchronously, which saves the communication cost and reduces the number of communications between nodes, so that the As the system throughput increases, the impact of network scale on performance becomes smaller. The simulation shows that the performance of the algorithm is improved by 50% compared with the PBFT algorithm. After the node reaches the maximum number of connections, the algorithm is limited by the size of the node and becomes smaller, and the scalability of the system is improved.
In recent years, blockchain technology has experienced tremendous growth in application development and gained a lot of attention from people. Blockchain technology-based applications are rapidly expanding in a variety of domains, including government, education, the energy sector, and the internet of things (IoT), among many others. Blockchain is a decentralised and peer-to-peer system that permits the recording of digital transactions in a distributed, encrypted, and secure manner. Even though blockchain technology has the potential to provide more efficient and reliable applications, challenges and obstacles are also present. This paper gives a rigorous overview of the underlying concepts of blockchain: distributed ledger technology, smart contracts, and other features; with the challenges and the corresponding solutions also discussed at the end. This paper also surveyed the blockchain applications in the education ecosystem that are tied to student credentials.
Kalapatapu V. S. K. R. Shiva Kumar, Shriram K. Vasudevan, Nitin Vamsi Dantu
The COVID-19 pandemic has shocked the globe with an enormous number of people infected and a large death toll across several nations. A deadly virus has many victims but no country could stand out when it comes to producing a vaccine. The virus is so dangerous that it spreads rapidly through human contact and a person who is infected will infect around 600 people a month. It is so fast that more than 50,000 people are affected in one day in some countries and more than 1,000 people die in one day. There are many patients but not enough doctors and hospitals to treat them as the infection grows exponentially. No doctor can examine chest X-ray in thousands and have fast turnaround. We want to create a solution to reduce the workload on doctors, to easily determine whether a chest X-ray pneumonia is due to coronavirus or not, so that the rapid spread can be controlled and proper cure could be given to patients. Here we also add the distributed ledger technology called blockchain, which helps in monitoring the patient health data and thus it helps in having the complete history of the patient.
Paras Jain, Sunita Dwivedi, Adel R. Alharbi, R. Sureshbabu · 7 authors
Through the use of blockchain technology, sensitive information may be securely communicated without the need to replicate it, which can assist in decreasing medical record mistakes and saving time by eliminating the need to duplicate information. Furthermore, the information is timestamped, which further enhances the security of the data even further. The deployment of blockchain technology in a range of healthcare situations may enhance the security and efficiency of payment transactions. In this way, only those who have been allowed access to patient medical information can see or modify such information. It is proposed in this study that blockchain technology be used to provide an accessible data storage and retrieval mechanism for patients and healthcare professionals in a healthcare system that is both safe and efficient. As of 1970, a variety of traditional knowledge‐based approaches such as Personal Identification Recognition Number (PIRN), passwords, and other similar methods have been made available; however, many token‐based approaches such as drivers’ licenses, passports, credit cards, bank accounts, ID cards, and keys have also been made available; however, they have all failed to establish a secure and reliable transaction channel. Because they are easily misplaced, stolen, or lost, they are usually unable to protect secrecy or authenticate the identity of a legitimate claimant. Aside from that, personally identifiable information such as passwords and PINs is very prone to fraud since they are easily forgotten or guessed by an imposter. Biometric identification and authentication (commonly known as biometrics) are attracting a great deal of attention these days, particularly in the realm of information security systems, due to its inherent potential and advantages over other conventional ways for identifying and authenticating. As a result of the device’s unique biological characteristics, which include features such as fingerprints, facepalms, hand geometry (including the iris), and the device’s iris, it can be used in a variety of contexts, such as consumer banking kiosks, airport security systems, international ports of entry, universities, office buildings, and forensics, to name a few. It is also used in several other contexts, including forensics and law enforcement. Consequently, every layer of the system—sensed data, computation, and processing of data, as well as the storage and administration of data—is susceptible to a broad variety of threats and weaknesses (cloud). There does not seem to be any suitable methods for dealing with the large volumes of data created by the fog computing architecture when normal data storage and security technologies are used. Because of this, the major objective of this research is to design security countermeasures against medical data mining vulnerabilities that originate from the sensing layer and data storage in the Internet of Things’ cloud database, both of which are discussed in more depth further down. A key allows for the creation of a distributed ledger database and provides an immutable security solution, transaction transparency, and the prohibition of tampering with patient information. This mechanism is particularly useful in healthcare settings, where patient information must be kept confidential. When used in a hospital environment, this method is extremely beneficial. As a result of incorporating blockchain technology into the fog paradigm, it is possible to alleviate some of the current concerns associated with latency, centralization, and scalability.