Deep Learning Based Homomorphic Secure Search-Able Encryption for Keyword Search in Blockchain Healthcare System: A Novel Approach to Cryptography

Due to the value and importance of patient health records (PHR), security is the most critical feature of encryption over the Internet. Users that perform keyword searches to gain access to the PHR stored in the database are more susceptible to security risks. Although a blockchain-based healthcare system can guarantee security, present schemes have several flaws. Existing techniques have concentrated exclusively on data storage and have utilized blockchain as a storage database. In this research, we developed a unique deep-learning-based secure search-able blockchain as a distributed database using homomorphic encryption to enable users to securely access data via search. Our suggested study will increasingly include secure key revocation and update policies. An IoT dataset was used in this research to evaluate our suggested access control strategies and compare them to benchmark models. The proposed algorithms are implemented using smart contracts in the hyperledger tool. The suggested strategy is evaluated in comparison to existing ones. Our suggested approach significantly improves security, anonymity, and monitoring of user behavior, resulting in a more efficient blockchain-based IoT system as compared to benchmark models.

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Cloud and IoT based privacy preserved e-Healthcare system using secured storage algorithm and deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-191490 ◽

2020 ◽

Vol 39 (3) ◽

pp. 3011-3023

Author(s):

T. Munirathinam ◽

Sannasi Ganapathy ◽

Arputharaj Kannan

Keyword(s):

Deep Learning ◽

Disease Severity ◽

Healthcare System ◽

Data Storage ◽

Original Data ◽

Medical Data ◽

Heart Beat ◽

Current Status ◽

Learning Approach ◽

Proposed Model

Rapid introduction of new diseases and the severity improvement of existing dead diseases due to the bad food habits and lacking of awareness over the health conscious food items those are available in the market. The Internet of Things (IoT) gets more attention for reducing the disease severity by knowing the current status of their disease according to the dynamic inputs of human body through IoT devices today. Moreover, the combination of IoT and cloud computing technologies are playing major roles in e-health services. In this scenario, security is a major issue in the process of data storage and communication. For this purpose, we propose a new e-healthcare system for monitoring the dead disease level by using the technologies such as IoT and Cloud with the help of deep learning approach and fuzzy rules with temporal features. In this system, the medical data is retrieved from various located patients who are utilizing the e-healthcare assisting devices. First, the retrieved and encrypted data is stored in cloud by applying a newly proposed secured cloud storage algorithm. Second, the stored data can be retrieved the data as original data by applying the decryption process. Third, a new cloud framework is introduced for predicting the status of heart beat rates and diabetes levels by using the medical data that is created by applying the UCI Repository dataset. In addition, a new deep learning approach which applies the Convolutional Neural Network for predicting the disease severity. The experimental results are obtained by conducting various experiments for the proposed model by using the dataset and the hospital patient records. The proposed model results outperforms the available disease prediction systems in terms of prediction accuracy.

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An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network

Sensors ◽

10.3390/s22020572 ◽

2022 ◽

Vol 22 (2) ◽

pp. 572

Author(s):

Aitizaz Ali ◽

Mohammed Amin Almaiah ◽

Fahima Hajjej ◽

Muhammad Fermi Pasha ◽

Ong Huey Fang ◽

...

Keyword(s):

Neural Network ◽

Data Storage ◽

Homomorphic Encryption ◽

Data Access ◽

Distributed Database ◽

Healthcare Systems ◽

The Internet ◽

Neural Network Approach ◽

Healthcare Data ◽

Industrial Iot

The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.

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A Deep Learning-based Smart Healthcare System for Patient’s Discomfort Detection at the Edge of Internet of Things

IEEE Internet of Things Journal ◽

10.1109/jiot.2021.3052067 ◽

2021 ◽

pp. 1-1

Author(s):

Imran Ahmed ◽

Gwanggil Jeon ◽

Francesco Piccialli

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Healthcare System ◽

Smart Healthcare

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Diabetes classification application with efficient missing and outliers data handling algorithms

Complex & Intelligent Systems ◽

10.1007/s40747-021-00349-2 ◽

2021 ◽

Author(s):

Hanaa Torkey ◽

Elhossiny Ibrahim ◽

EZZ El-Din Hemdan ◽

Ayman El-Sayed ◽

Marwa A. Shouman

Keyword(s):

Deep Learning ◽

Healthcare System ◽

Wearable Sensors ◽

Model Performance ◽

Analysis Model ◽

Human Errors ◽

Web Based ◽

Machine Learning Model ◽

Complete Dataset ◽

Smart Wearable

AbstractCommunication between sensors spread everywhere in healthcare systems may cause some missing in the transferred features. Repairing the data problems of sensing devices by artificial intelligence technologies have facilitated the Medical Internet of Things (MIoT) and its emerging applications in Healthcare. MIoT has great potential to affect the patient's life. Data collected from smart wearable devices size dramatically increases with data collected from millions of patients who are suffering from diseases such as diabetes. However, sensors or human errors lead to missing some values of the data. The major challenge of this problem is how to predict this value to maintain the data analysis model performance within a good range. In this paper, a complete healthcare system for diabetics has been used, as well as two new algorithms are developed to handle the crucial problem of missed data from MIoT wearable sensors. The proposed work is based on the integration of Random Forest, mean, class' mean, interquartile range (IQR), and Deep Learning to produce a clean and complete dataset. Which can enhance any machine learning model performance. Moreover, the outliers repair technique is proposed based on dataset class detection, then repair it by Deep Learning (DL). The final model accuracy with the two steps of imputation and outliers repair is 97.41% and 99.71% Area Under Curve (AUC). The used healthcare system is a web-based diabetes classification application using flask to be used in hospitals and healthcare centers for the patient diagnosed with an effective fashion.

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Lensless phase retrieval based on deep learning used in holographic data storage

Optics Letters ◽

10.1364/ol.433955 ◽

2021 ◽

Author(s):

Jianying Hao ◽

Xiao Lin ◽

Yongkun Lin ◽

Haiyang Song ◽

ruixian chen ◽

...

Keyword(s):

Deep Learning ◽

Data Storage ◽

Phase Retrieval ◽

Holographic Data Storage

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Using a distributed deep learning algorithm for analyzing big data in smart cities

Smart and Sustainable Built Environment ◽

10.1108/sasbe-04-2019-0040 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Mohammed Anouar Naoui ◽

Brahim Lejdel ◽

Mouloud Ayad ◽

Abdelfattah Amamra ◽

Okba kazar

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analysis ◽

Data Storage ◽

Smart City ◽

Smart Cities ◽

Smart Environment ◽

Data Systems ◽

Content Type ◽

Big Data Systems

PurposeThe purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.Design/methodology/approachWe have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.FindingsWe apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.Research limitations/implicationsThis research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.Practical implicationsFindings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.Originality/valueThe findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.

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Video Data Integrity Verification Method Based on Full Homomorphic Encryption in Cloud System

International Journal of Digital Multimedia Broadcasting ◽

10.1155/2018/7543875 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Ruoshui Liu ◽

Jianghui Liu ◽

Jingjie Zhang ◽

Moli Zhang

Keyword(s):

Cloud Computing ◽

Data Storage ◽

Homomorphic Encryption ◽

Data Integrity ◽

Video Data ◽

Verification Method ◽

Whole Process ◽

Integrity Checking ◽

Verification Methods ◽

Cloud Servers

Cloud computing is a new way of data storage, where users tend to upload video data to cloud servers without redundantly local copies. However, it keeps the data out of users' hands which would conventionally control and manage the data. Therefore, it becomes the key issue on how to ensure the integrity and reliability of the video data stored in the cloud for the provision of video streaming services to end users. This paper details the verification methods for the integrity of video data encrypted using the fully homomorphic crytosystems in the context of cloud computing. Specifically, we apply dynamic operation to video data stored in the cloud with the method of block tags, so that the integrity of the data can be successfully verified. The whole process is based on the analysis of present Remote Data Integrity Checking (RDIC) methods.

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New Algorithms for Secure Outsourcing of Large-Scale Systems of Linear Equations

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.d1149.039520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 545-550

Keyword(s):

Cloud Computing ◽

Distributed Computing ◽

Data Storage ◽

Large Scale ◽

Homomorphic Encryption ◽

Linear Equations ◽

Force Transmission ◽

Large Scale Systems ◽

Secure Outsourcing ◽

Systems Of Linear Equations

Cloud computing is the on-request accessibility of computer system resources, specially data storage and computing power, without direct dynamic management by the client. In the simplest terms, cloud computing means storing and accessing data and programs over the Internet instead of your computer’s hard drive. Along the improvement of cloud computing, more and more applications are migrated into the cloud. A significant element of distributed computing is pay-more only as costs arise. Distributed computing gives strong computational capacity to the general public at diminished cost that empowers clients with least computational assets to redistribute their huge calculation outstanding burdens to the cloud, and monetarily appreciate the monstrous computational force, transmission capacity, stockpiling, and even reasonable programming that can be partaken in a compensation for each utilization way Tremendous bit of leeway is the essential objective that forestalls the wide scope of registering model for clients when their secret information are expended during the figuring procedure. Critical thinking is a system to arrive at the pragmatic objective of specific instruments that tackles the issues as well as shield from pernicious practices.. In this paper, we examine secure outsourcing for large-scale systems of linear equations, which are the most popular problems in various engineering disciplines. Linear programming is an operation research technique formulates private data by the customer for LP problem as a set of matrices and vectors, to develop a set of efficient privacypreserving problem transformation techniques, which allow customers to transform original LP problem into some arbitrary one while protecting sensitive input/output information. Identify that LP problem solving in Cloud component is efficient extra cost on cloud server. In this paper we are utilizing Homomorphic encryption system to increase the performance and time efficiency

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Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a9961.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 608-612

Keyword(s):

Neural Network ◽

Resource Allocation ◽

Deep Learning ◽

Convolutional Neural Network ◽

Data Storage ◽

Memory Management ◽

Research Work ◽

Dynamic Resource Allocation ◽

Mobile Platform ◽

Dynamic Resource

Memory management is very essential task for large-scale storage systems; in mobile platform generate storage errors due to insufficient memory as well as additional task overhead. Many existing systems have illustrated different solution for such issues, like load balancing and load rebalancing. Different unusable applications which are already installed in mobile platform user never access frequently but it allocates some memory space on hard device storage. In the proposed research work we describe dynamic resource allocation for mobile platforms using deep learning approach. In Real world mobile systems users may install different kind of applications which required ad-hoc basis. Such applications may be affect to execution performance of system as well space complexity, sometime they also affect another runnable applications performance. To eliminate of such issues, we carried out an approach to allocate runtime resources for data storage for mobile platform. When system connected with cloud data server it store complete file system on remote Virtual Machine (VM) and whenever a single application required which immediately install beginning as remote server to local device. For developed of proposed system we implemented deep learning base Convolutional Neural Network (CNN), algorithm has used with tensorflow environment which reduces the time complexity for data storage as well as extraction respectively.

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Leveraging coherent wave field analysis and deep learning in fiber-optic seismology

10.5194/egusphere-egu21-7856 ◽

2021 ◽

Author(s):

Benjamin Schwarz ◽

Korbinian Sager ◽

Philippe Jousset ◽

Gilda Currenti ◽

Charlotte Krawczyk ◽

...

Keyword(s):

Deep Learning ◽

Wave Field ◽

Real Time ◽

Learning Strategies ◽

Data Storage ◽

Large Scale ◽

Fiber Optic ◽

Positive Impact ◽

Field Analysis ◽

Coherent Wave

Fiber-optic cables form an integral part of modern telecommunications infrastructure and are ubiquitous in particular in regions where dedicated seismic instrumentation is traditionally sparse or lacking entirely. Fiber-optic seismology promises to enable affordable and time-extended observations of earth and environmental processes at an unprecedented temporal and spatial resolution. The method&#8217;s unique potential for combined large-N and large-T observations implies intriguing opportunities but also significant challenges in terms of data storage, data handling and computation.Our goal is to enable real-time data enhancement, rapid signal detection and wave field characterization without the need for time-demanding user interaction. We therefore combine coherent wave field analysis, an optics-inspired processing framework developed in controlled-source seismology, with state-of-the-art deep convolutional neural network (CNN) architectures commonly used in visual perception. While conventional deep learning strategies have to rely on manually labeled or purely synthetic training datasets, coherent wave field analysis labels field data based on physical principles and enables large-scale and purely data-driven training of the CNN models. The shear amount of data already recorded in various settings makes artificial data generation by numerical modeling superfluous &#8211; a task that is often constrained by incomplete knowledge of the embedding medium and an insufficient description of processes at or close to the surface, which are challenging to capture in integrated simulations.Applications to extensive field datasets acquired with dark-fiber infrastructure at a geothermal field in SW Iceland and in a town at the flank of Mt Etna, Italy, reveal that the suggested framework generalizes well across different observational scales and environments, and sheds new light on the origin of a broad range of physically distinct wave fields that can be sensed with fiber-optic technology. Owing to the real-time applicability with affordable computing infrastructure, our analysis lends itself well to rapid on-the-fly data enhancement, wave field separation and compression strategies, thereby promising to have a positive impact on the full processing chain currently in use in fiber-optic seismology.

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