Privacy-Preserving Healthcare and Medical Data Collaboration Service System Based on Blockchain and Federated Learning

As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.

Anomaly Detection Using Data Rate of Change on Medical Data

The identification and mitigation of anomaly data,characterized by deviations from normal patterns or singularities,stand as critical endeavors in modern technological landscapes,spanning domains such as Non-Fungible Tokens(NFTs),cyber-security,and the burgeoning metaverse.This paper presents a novel proposal aimed at refining anomaly detection methodologies,with a particular focus on continuous data streams.The essence of the proposed approach lies in analyzing the rate of change within such data streams,leveraging this dynamic aspect to discern anomalies with heightened precision and efficacy.Through empirical evaluation,our method demonstrates a marked improvement over existing techniques,showcasing more nuanced and sophisticated result values.Moreover,we envision a trajectory of continuous research and development,wherein iterative refinement and supplementation will tailor our approach to various anomaly detection scenarios,ensuring adaptability and robustness in real-world applications.

Energy Aware Clustering with Medical Data Classification Model in IoT Environment

With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced services.Due to the limited energy capacity of IoT devices,energy-aware clustering techniques can be highly preferable.At the same time,artificial intelligence(AI)techniques can be applied to perform appropriate disease diagnostic processes.With this motivation,this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification(SSAC-MDC)model in an IoT environment.The goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT environment.The proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering(SSAC)technique to choose the proper set of cluster heads(CHs)and construct clusters.Besides,the medical data classification process involves three different subprocesses namely pre-processing,autoencoder(AE)based classification,and improved beetle antenna search(IBAS)based parameter tuning.The design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the work.For show-casing the improved performance of the SSAC-MDC technique,a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.

An Improved Steganographic Scheme Using the Contour Principle to Ensure the Privacy of Medical Data on Digital Images

With the improvement of current online communication schemes,it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate.Traditional steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before transmission.Both of the techniques mentioned above aid in the confidentiality of feature content.The proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red,Green,and Blue.The private Content originated from a medical client and was forwarded to a medical practitioner on the server end through the internet.The K-Means clustering principle uses the contouring approach to frame the pixel clusters on the image layers.The content embodiment procedure is performed on the selected pixel groups of all layers of the image using the Least Significant Bit(LSB)substitution technique to build the secret Content embedded image known as the stego image,which is subsequently transmitted across the internet medium to the server end.The experimental results are computed using the inputs from“Open-Access Medical Image Repositories(aylward.org)”and demonstrate the scheme’s impudence as the Content concealing procedure progresses.

Ensemble Deep Learning with Chimp Optimization Based Medical Data Classification

Eye state classification acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so on.The modifications in the cognitive levels can be reflected via transforming the electro-encephalogram(EEG)signals.The deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-sification model in the recognition processes.This paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classifi-cation(EDLCOA-ESC).The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing step.Besides,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG signals.In addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classification.Finally,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classification results to a maximum extent.An extensive range of simulation analysis on the benchmark dataset is car-ried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.

IWD-Miner: A Novel Metaheuristic Algorithm for Medical Data Classification

Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis.To gain experts’trust,the prediction and the reasoning behind it are equally important.Accordingly,we confine our research to learn rule-based models because they are transparent and comprehensible.One approach to MDC involves the use of metaheuristic(MH)algorithms.Here we report on the development and testing of a novel MH algorithm:IWD-Miner.This algorithm can be viewed as a fusion of Intelligent Water Drops(IWDs)and AntMiner+.It was subjected to a four-stage sensitivity analysis to optimize its performance.For this purpose,21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine.Interestingly,there were only limited differences in performance between IWDMiner variants which is suggestive of its robustness.Finally,using the same 21 datasets,we compared the performance of the optimized IWD-Miner against two extant algorithms,AntMiner+and J48.The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner,as confirmed by the Wilcoxon nonparametric statistical test.Results suggest that IWD-Miner is more efficient than AntMiner+as measured by the average number of fitness evaluations to a solution(1,386,621.30 vs.2,827,283.88 fitness evaluations,respectively).J48 exhibited higher accuracy on average than IWD-Miner(79.58 vs.73.65,respectively)but produced larger models(32.82 leaves vs.8.38 terms,respectively).

Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making process.The latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification models.At the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality problem.For resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(FSS-AICBD)technique.The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results.Primarily,the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity.In addition,the information gain(IG)approach is applied for the optimal selection of feature subsets.Also,group search optimizer(GSO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm.The choice of IG and GSO approaches results in promising medical data classification results.The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets.The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.

Fuzzy Logic with Archimedes Optimization Based Biomedical Data Classification Model

Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making.Besides,the advances of machine learning(ML)techniques assist to perform the effective classification task.With this motivation,this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm(FCA-BFS)with optimal support vector machine(OSVM)model,named FCABFS-OSVM for medical data classification.The proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification models.Besides,the proposed FCABFSOSVM technique involves the design of FCABFS technique to cluster the medical data which helps to boost the classification performance.Moreover,the OSVM model investigates the clustered medical data to perform classification process.Furthermore,Archimedes optimization algorithm(AOA)is utilized to the SVM parameters and boost the medical data classification results.A wide range of simulations takes place to highlight the promising performance of the FCABFS-OSVM technique.Extensive comparison studies reported the enhanced outcomes of the FCABFS-OSVM technique over the recent state of art approaches.

Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)systems.QC has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds.So,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data prediction.In this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical data.The output of optimization is either 0 or 1 i.e.,odd or even in QC.From this output value,the data is identified according to the class.Meanwhile,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter method.After the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by LTSA.The motivation of the minimal objective is to remain faster.However,some computations can be performed more efficiently by the proposed model.In testing,the test data was evaluated by minimal loss function.The outcomes were assessed in terms of accuracy,computational time,and so on.For this,databases like Lymphography,Dermatology,and Arrhythmia were used.

Secure Model of Medical Data Sharing for Complex Scenarios

In order to secure the massive heterogeneous medical data for the complex scenarios and improve the information sharing efficiency in healthcare system,a distributed medical data ledger model(DMDL)is proposed in this paper.This DMDL model has adopted the blockchain technology including the function decoupling,the distributed consensus,smart contract as well as multi-channel communication structure of consortium blockchain.The DMDL model not only has high adaptability,but also meets the requirements of the medical treatment processes which generally involve multientities,highly private information and secure transaction.The steps for processing the medical data are also introduced.Additionally,the methods for the definition and application of the DMDL model are presented for three specific medical scenarios,i.e.,the management of the heterogeneous data,copyright protection for medical data and the secure utilization of sensitive data.The advantage of the proposed DMDL model is demonstrated by comparing with the models which are being currently adopted in healthcare system.

MEDICLOUD:a holistic study on the digital evolution of medical data

The Corona Virus Disease 2019(COVID-19) pandemic has taught us many valuable lessons regarding the importance of our physical and mental health. Even with so many technological advancements, we still lag in developing a system that can fully digitalize the medical data of each individual and make it readily accessible for both the patient and health worker at any point in time. Moreover, there are also no ways for the government to identify the legitimacy of a particular clinic. This study merges modern technology with traditional approaches,thereby highlighting a scenario where artificial intelligence(AI) merges with traditional Chinese medicine(TCM), proposing a way to advance the conventional approaches. The main objective of our research is to provide a one-stop platform for the government, doctors,nurses, and patients to access their data effortlessly. The proposed portal will also check the doctors’ authenticity. Data is one of the most critical assets of an organization, so a breach of data can risk users’ lives. Data security is of primary importance and must be prioritized. The proposed methodology is based on cloud computing technology which assures the security of the data and avoids any kind of breach. The study also accounts for the difficulties encountered in creating such an infrastructure in the cloud and overcomes the hurdles faced during the project, keeping enough room for possible future innovations. To summarize, this study focuses on the digitalization of medical data and suggests some possible ways to achieve it. Moreover, it also focuses on some related aspects like security and potential digitalization difficulties.

IHVFL: a privacy-enhanced intention-hiding vertical federated learning framework for medical data

Vertical Federated Learning(VFL)has many applications in the field of smart healthcare with excellent performance.However,current VFL systems usually primarily focus on the privacy protection during model training,while the preparation of training data receives little attention.In real-world applications,like smart healthcare,the process of the training data preparation may involve some participant's intention which could be privacy information for this partici-pant.To protect the privacy of the model training intention,we describe the idea of Intention-Hiding Vertical Feder-ated Learning(IHVFL)and illustrate a framework to achieve this privacy-preserving goal.First,we construct two secure screening protocols to enhance the privacy protection in feature engineering.Second,we implement the work of sample alignment bases on a novel private set intersection protocol.Finally,we use the logistic regression algorithm to demonstrate the process of IHVFL.Experiments show that our model can perform better efficiency(less than 5min)and accuracy(97%)on Breast Cancer medical dataset while maintaining the intention-hiding goal.

Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

Large-scale wireless sensor networks(WSNs)play a critical role in monitoring dangerous scenarios and responding to medical emergencies.However,the inherent instability and error-prone nature of wireless links present significant challenges,necessitating efficient data collection and reliable transmission services.This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs.The primary goal is to enhance the reliability of data collection and transmission services,ensuring a comprehensive and practical approach.Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability.Additionally,it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops.These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios.Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes.The evaluation spans varying sensor densities,wireless channel conditions,and packet transmission rates,showcasing the protocol’s superiority in ensuring reliable and efficient data transfer.Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs.By prioritizing fairness,reliability,and efficiency,the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services,thereby offering a valuable contribution to the field of medical event-drivenWSNs.

Changes and Adjustments:The Rule of Law Response to Medical Institution Data Compliance

Medical institution data compliance is an exogenous product of the digital society,serving as a crucial means to maintain and balance the relationship between data protection and data sharing,as well as individual interests and public interests.The implementation of the Healthy China Initiative greatly benefits from its practical significance.In practice,data from medical institutions takes varied forms,including personally identifiable data collected before diagnosis and treatment,clinical medical data generated during diagnosis and treatment,medical data collected in public health management,and potential medical data generated in daily life.In the new journey of comprehensively promoting the Chinese path to modernization,it is necessary to clarify the shift from an individual-oriented to a societal-oriented value system,highlighting the reinforcing role of the trust concept.Guided by the principle of minimizing data utilization,the focus is on the new developments and changes in medical institution data in the postpandemic era.This involves a series of measures such as fulfilling the obligation of notification and consent,specifying the scope of data collection and usage,strengthening the standardized use of relevant technical measures,and establishing a sound legal responsibility system for data compliance.Through these measures,a flexible and efficient medical institution data compliance system can be constructed.

Pre-training in Medical Data:A Survey

Medical data refers to health-related information associated with regular patient care or as part of a clinical trial program.There are many categories of such data,such as clinical imaging data,bio-signal data,electronic health records(EHR),and multi-modality medical data.With the development of deep neural networks in the last decade,the emerging pre-training paradigm has become dominant in that it has significantly improved machine learning methods′performance in a data-limited scenario.In recent years,studies of pre-training in the medical domain have achieved significant progress.To summarize these technology advancements,this work provides a comprehensive survey of recent advances for pre-training on several major types of medical data.In this survey,we summarize a large number of related publications and the existing benchmarking in the medical domain.Especially,the survey briefly describes how some pre-training methods are applied to or developed for medical data.From a data-driven perspective,we examine the extensive use of pre-training in many medical scenarios.Moreover,based on the summary of recent pre-training studies,we identify several challenges in this field to provide insights for future studies.

Research on medical data storage and sharing model based on blockchain

With the process of medical informatization,medical diagnosis results are recorded and shared in the form of electronic data in the computer.However,the security of medical data storage cannot be effectively protected and the unsafe sharing of medical data among different institutions is still a hidden danger that cannot be underestimated.To solve the above problems,a secure storage and sharing model of private data based on blockchain technology and homomorphic encryption is constructed.Based on the idea of blockchain decentralization,the model maintains a reliable medical alliance chain system to ensure the safe transmission of data between different institutions;A privacy data encryption and computing protocol based on homomorphic encryption is constructed to ensure the safe transmission of medical data;Using its complete anonymity to ensure the Blockchain of medical data and patient identity privacy;A strict transaction control management mechanism of medical data based on Intelligent contract automatic execution of preset instructions is proposed.After security verification,compared with the traditional medical big data storage and sharing mode,the model has better security and sharing.

Research on a Fog Computing Architecture and BP Algorithm Application for Medical Big Data

Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficiency of medical diagnosis.And with the wide application of the Internet of Things and Big Data in the medical field,medical Big Data is increasing in geometric magnitude resulting in cloud service overload,insufficient storage,communication delay,and network congestion.In order to solve these medical and network problems,a medical big-data-oriented fog computing architec-ture and BP algorithm application are proposed,and its structural advantages and characteristics are studied.This architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate,compare and analyze the fog node through the Internet of Things.The diagnosis results are designed to reduce the business processing delay and improve the diagnosis effect.Considering the weak computing of each edge device,the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power,enhance the medical intelligence-aided decision-making,and improve the clinical diagnosis and treatment efficiency.In the application process,combined with the characteristics of medical Big Data technology,through fog architecture design and Big Data technology integration,we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of Things.The results are promising:The medical platform network is smooth,the data storage space is sufficient,the data processing and analysis speed is fast,the diagnosis effect is remarkable,and it is a good assistant to doctors’treatment effect.It not only effectively solves the problem of low clinical diagnosis,treatment efficiency and quality,but also reduces the waiting time of patients,effectively solves the contradiction between doctors and patients,and improves the medical service quality and management level.

Impact of preprocessing on medical data classification

The significance of the preprocessing stage in any data mining task is well known. Before attempting medical data classification, characteristics of medical datasets, including noise, incompleteness, and the existence of multiple and possibly irrelevant features, need to be addressed. In this paper, we show that selecting the right combination of prepro- cessing methods has a considerable impact on the classification potential of a dataset. The preprocessing operations con- sidered include the discretization of numeric attributes, the selection of attribute subset（s）, and the handling of missing values. The classification is performed by an ant colony optimization algorithm as a case study. Experimental results on 25 real-world medical datasets show that a significant relative improvement in predictive accuracy, exceeding 60% in some cases, is obtained.

Improvement of Association Rule Algorithm Based on Hadoop for Medical Data

Data mining technology and association rule mining can be important technologies to deal with a large amount of accumulated data in the medical field,and can reflect the value of large medical data.According to the characteristics of large medical data,aiming at the problem that the traditional Apriori algorithm scans the database too long and generates too many candidate itemsets,a method of digital mapping and sorting of itemsets is proposed.The method of the base model and generation model was used to generate superset,which can improve the efficiency of superset generation and pruning.By using open source framework Hadoop and transplanting the improved algorithm to the Hadoop platform combined with the MapReduce framework,the idea of parallel improvement was introduced based on database partition.Experimental results show that it solves the redundancy of large-scale data sets and makes Apriori algorithm have good parallel scalability.Finally,an example was given to demonstrate the possibility of improving the algorithm.

Multi Attribute Case Based Privacy-preserving for Healthcare Transactional Data Using Cryptography

Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy.In this background,several authentication and accessibility issues emerge with an inten-tion to protect the sensitive details of the patients over getting published in open domain.To solve this problem,Multi Attribute Case based Privacy Preservation(MACPP)technique is proposed in this study to enhance the security of privacy-preserving data.Private information can be any attribute information which is categorized as sensitive logs in a patient’s records.The semantic relation between transactional patient records and access rights is estimated based on the mean average value to distinguish sensitive and non-sensitive information.In addition to this,crypto hidden policy is also applied here to encrypt the sensitive data through symmetric standard key log verification that protects the personalized sensitive information.Further,linear integrity verification provides authentication rights to verify the data,improves the performance of privacy preserving techni-que against intruders and assures high security in healthcare setting.