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

Sensors ◽  
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.

Using Artificial Neural Network to Filter Spam for Chinese Mail

2011 ◽  
Vol 55-57 ◽  
pp. 762-766
Author(s):  
Shih Ming Pi ◽  
Hsiu Li Liao ◽  
Su Houn Liu ◽  
Ding Kang Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Text Classification ◽  
False Positives ◽  
The Internet ◽  
Network Approach ◽  
Good Tool ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Computing Speed

As the Internet developed, the problem of spam has become increasingly serious. Not only caused great distress to individuals, but also have a great business costs. With improvements in computing speed, neural network is becoming a very good tool for text classification. The purpose of this study is to conduct few experiments by using neural network approach for Chinese mails’ content. The result shows that neural network approach is effective for Chinese mails’ spam-identification and the adjustments of some parameters (the number of keywords, the number of nodes, and the number of categories) also increase the accurate rate, while reducing false positives.

Taking control of your own pervasive mHealth Data: A Proof of Concept using a configurable Blockchain and mHealth application on iOS and Android devices (Preprint)

2021 ◽  
Author(s):  
Arijit Sengupta ◽  
Hemang Chamakuzhi Subramanian
Keyword(s):  
Pervasive Computing ◽  
Design Science ◽  
Single Point ◽  
Data Access ◽  
Healthcare Systems ◽  
Personalized Healthcare ◽  
Healthcare Data ◽  
User Data ◽  
Hipaa Compliance ◽  
System Interoperability

BACKGROUND Background: Blockchains offer a promising new distributed technology to address the challenges of data standardization, system interoperability, security, privacy, and accessibility for all data. However, integrating pervasive computing with blockchain’s ability to store privacy-protected mHealth data while providing HIPAA compliance is a challenge. Patients use a multitude of devices, apps, and services to collect and store mHealth data. Before the advent of blockchains, providing anonymized privacy controlled single point of access for different data sources for each user was a challenging problem. We present the design of an IoT-based configurable blockchain with different mHealth applications on iOS and Android collecting the same user’s data. We discuss the advantages of using such a blockchain architecture and demonstrate two things – the ease with which users can retain full control of their pervasive mHealth data and the ease with which HIPAA compliance can be accomplished by provider(s) who choose to access user data. We also allude to the future of shareable and tradeable data with our paper. OBJECTIVE Objective: The purpose of this paper is to design, evaluate and test IoT-based mHealth data using wearable devices using an efficient configurable blockchain designed and implemented ground up to store such data. The purpose of this paper is to demonstrate the privacy-preserving and HIPAA-compliant nature of pervasive computing-based personalized healthcare systems that give users total control of their own data. METHODS Methods: This paper followed the methodical design science approach adapted in information systems wherein we evaluate prior designs, propose enhancements with a Blockchain design pattern published by the same author(s), and use the design to support IoT transactions. We prototype both the blockchain and the IoT-based mHealth applications in different devices and test all use cases that formed the design goals for such a system. Specifically, we validate the design goals for our system using the HIPAA checklist for businesses and prove compliance of our architecture for mHealth data on pervasive computing devices. RESULTS Results: Blockchain-based personalized healthcare systems provide several advantages over traditional systems. They support the following features: provide and support extreme privacy protection, ability to share personalized data, provide the ability to delete data upon request, and support the ability to work on data. CONCLUSIONS Conclusions: We conclude that blockchain(s) and specifically the CHASM architecture presented in this paper, with configurable module(s) and a Software as a service Model provide many advantages for patients using pervasive devices that store mHealth data on the blockchain. Among them, is the ability to store, retrieve and modify one(s) generated healthcare data with a single private key across devices. This data is transparent and stored perennially and provides patients the privacy and pseudo-anonymity in addition to very strong encryption for data access. Firms and Device manufacturers would be benefited from such an approach wherein they relinquish user data control, while giving users the ability to select and offer their own mHealth data on data marketplaces. We show that such an architecture complies with the stringent requirements of HIPAA for patient data access.

TBHM

2020 ◽  
Vol 15 (3) ◽  
pp. 1-17
Author(s):  
Lalit Mohan Gupta ◽  
Abdus Samad ◽  
Hitendra Garg
Keyword(s):  
Data Storage ◽  
Homomorphic Encryption ◽  
Decision Makers ◽  
Key Generation ◽  
Time Error ◽  
Health Records ◽  
Medical Practitioners ◽  
Healthcare Data ◽  
Insurance Firms ◽  
Encryption Decryption

Healthcare today is one of the most promising, prevailing, and sensitive sectors where patient information like prescriptions, health records, etc., are kept on the cloud to provide high quality on-demand services for enhancing e-health services by reducing the burden of data storage and maintenance to providing information independent of location and time. The major issue with healthcare organization is to provide protected sharing of healthcare data from the cloud to the decision makers, medical practitioners, data analysts, and insurance firms by maintaining confidentiality and integrity. This article proposes a novel and secure threshold based encryption scheme combined with homomorphic properties (TBHM) for accessing cloud based health information. Homomorphic encryption completely eliminates the possibility of any kind of attack as data cannot be accessed using any type of key. The experimental results report superiority of TBHM scheme over state of art in terms throughput, file encryption/decryption time, key generation time, error rate, latency time, and security overheads.

AGENTS FOR QUERYING DISTRIBUTED STATISTICAL DATABASES OVER THE INTERNET

2002 ◽  
Vol 11 (01) ◽  
pp. 63-94 ◽  
Author(s):  
SALLY McCLEAN ◽  
ISAMBO KARALI ◽  
BRYAN SCOTNEY ◽  
KIERAN GREER ◽  
GEORGIOS-DIMITRIOS KAPOS ◽  
...  
Keyword(s):  
Statistical Data ◽  
Distributed Processing ◽  
Data Access ◽  
Distributed Database ◽  
The Internet ◽  
Data Harmonization ◽  
Data Matching ◽  
Distributed Approach ◽  
User Query ◽  
Multi Agent

Distributed database techniques and the Internet provide producers of statistics with a means to publish their data and metadata widely and make them available to a variety of users. Data matching to a user query and data access as well as data harmonization are some of the problems that should be solved. Intelligence is required in various stages of query answering and data matching. Moreover, the breadth and distributed nature of the Internet urge for a distributed approach. Agents seem to be the means by which both intelligence and distributed processing can be achieved. This paper presents a distributed approach for answering queries on statistical data that exist over the Internet using a multi-agent framework.

Neural network system for detecting and neutralizing remote unauthorized interference into the components of the Internet of Things

2021 ◽  
Author(s):  
A. I. Vlasov ◽  
E. R. Zakharov ◽  
V. O. Zakharova
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Normal Modes ◽  
The Internet ◽  
Network System ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Neural Network System ◽  
Artificial Neural ◽  
The Internet Of Things

In this work the authors have analyzed the neural network system for detecting and neutralizing remote and unauthorized interference with components of the Internet of Things. The main focus is on considering the neural network approach to detecting intrusions into the Internet of Things network, its monitoring and countering suspicious activity on the host. Features of development of model of artificial neural networks for application of apparatus of neural network in this direction have been considered. This allows you to reflect the successful identification of various types of attacks in terms of true and false positive results. However, the problems of obtaining data on overload and critical modes of the system remain unresolved. The use of a neural network system for detecting and neutralizing remote and unauthorized interference with components of the Internet of Things allows you to implement a module for detecting anomalies in the network, based on the Voltaire series, which considers the theoretical prerequisites of the method of dynamically building an artificial neural network. The main types of attacks, types of intrusion detection systems, interpretations of the obtained data, a brief study of works in the field of neural network solutions have been analyzed. An effective solution has been offered to protect workstations in the Internet of Things network from unauthorized access, and to configure security for all component modules. In conclusion, recommendations have been given for implementing the construction of a neural network module that detects deviations in the operation of the Internet of Things from normal modes.

scholarly journals Multi-objective Chaotic Butterfly Optimization with Deep Neural Network based Sustainable Healthcare Management Systems

2021 ◽  
pp. 39-48
Author(s):  
Abedallah Zaid Abualkishik ◽  
◽  
◽  
Ali A. Alwan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Simulation Analysis ◽  
Fitness Function ◽  
Healthcare Services ◽  
Healthcare Systems ◽  
Healthcare Management ◽  
Management Systems ◽  
Multi Objective ◽  
Healthcare Data

Sustainable healthcare systems are developed to priorities healthcare services involving difficult decision-making processes. Besides, wearables, internet of things (IoT), and cloud computing (CC) concepts are involved in the design of sustainable healthcare systems. In this study, a new Multi-objective Chaotic Butterfly Optimization with Deep Neural Network (MOCBOA-DNN) is presented for sustainable healthcare management systems. The goal of the MOCBOA-DNN technique aims to cluster the healthcare IoT devices and diagnose the disease using the collected healthcare data. The MOCBOA technique is derived to perform clustering process and also to tune the hyperparameters of the DNN model. Primarily, the clustering of IoT healthcare devices takes place using a fitness function to select an optimal set of cluster heads (CHs) and organize clusters. Followed by, the collected healthcare data are sent to the cloud server for further processing. Furthermore, the DNN model is used to investigate the healthcare data and thereby determine the presence of disease or not. In order to ensure the betterment of the MOCBOA-DNN technique, an extensive simulation analysis take place. The experimental results portrayed the supremacy of the MOCBOA-DNN technique over the other existing techniques interms of diverse evaluation parameters.

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

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 528
Author(s):  
Aitizaz Ali ◽  
Muhammad Fermi Pasha ◽  
Jehad Ali ◽  
Ong Huey Fang ◽  
Mehedi Masud ◽  
...  
Keyword(s):  
Deep Learning ◽  
Healthcare System ◽  
Data Storage ◽  
Keyword Search ◽  
User Behavior ◽  
Homomorphic Encryption ◽  
Distributed Database ◽  
Critical Feature ◽  
Suggested Approach ◽  
Secure Search

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.

The psychopharmacology of latent inhibition: A neural network approach

1997 ◽  
Author(s):  
Nestor A. Schmajuk ◽  
Catalin V. Buhusi ◽  
Jeffrey A. Gray
Keyword(s):  
Neural Network ◽  
Latent Inhibition ◽  
Network Approach ◽  
Neural Network Approach
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