Ensuring Data Integrity in Fog Computing Based Health-Care Systems

Alazeb, Abdulwahab; Panda, Brajendra

doi:10.1007/978-3-030-24907-6_6

Cited by 20 publications

(10 citation statements)

References 14 publications

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“…In a healthcare context, Alazeb and Panda [21]presented two separate frameworks for using fog computing.The two modelsare heterogeneous and homogeneous data from fog modules.They suggest a unique approach for each model to assess the harm done by malicious transactionsso that actual data may be retrieved, and transactions marked for potential inquiry can be impacted.In [22], a novel architecture called HealthFog is proposed to incorporate deep learning ensemble intoEdge computing devices and implemented it for real-life implementation of automated cardio disease detection. It offers healthcare as a fog service using IoT devices, handles heart patient information effectively, and comes as app requests.…”

Section: A Fog Computing In Healthcarementioning

confidence: 99%

Blockchain-Based Secure Healthcare Application for Diabetic-Cardio Disease Prediction in Fog Computing

Shynu

Menon²,

Kumar

et al. 2021

IEEE Access

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Fog computing is a modern computing model which offers geographically dispersed end-users with the latency-aware and highly scalable services. It is comparatively safer than cloud computing, due to information being rapidly stored and evaluated closer to data sources on local fog nodes. The advent of Blockchain (BC) technology has become a remarkable, most revolutionary, and growing development in recent years. BT's open platform stresses data protection and anonymity. It also guarantees data is protected and valid through the consensus process. BC is mainly used in money-related exchanges; now it will be used in many domains, including healthcare; This paper proposes efficient Blockchain-based secure healthcare services for disease prediction in fog computing. Diabetes and cardio diseases are considered for prediction. Initially, the patient health information is collected from Fog Nodes and stored on a Blockchain. The novel rule-based clustering algorithm is initially applied to cluster the patient health records. Finally, diabetic and cardio diseases are predicted using feature selection based adaptive neuro-fuzzy inference system (FS-ANFIS). To evaluate the performance of the proposed work, an extensive experiment and analysis were conducted on data from the real world healthcare. The experimental results show that the proposed work efficiently predict the disease.

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Section: A Fog Computing In Healthcarementioning

confidence: 99%

Blockchain-Based Secure Healthcare Application for Diabetic-Cardio Disease Prediction in Fog Computing

Shynu

Menon²,

Kumar

et al. 2021

IEEE Access

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“…Other works write about the architecture of the fog system [10]. Security and privacy issues are also a hot topic in this field [11], [12].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Alazeb and Panda [12] introduced two different models for using fog computing in a healthcare environment. The first model is an architecture that uses fog modules with heterogeneous data, and the second model uses fog modules with homogeneous data.…”

Section: Literature Reviewmentioning

confidence: 99%

Maintaining Data Integrity in Fog Computing Based Critical Infrastructure Systems

Alazeb

Panda

2019

2019 International Conference on Computational Science and Computational Intelligence (CSCI)

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The evolution of the utilization of technologies in nearly all aspects of life has produced an enormous amount of data essential in a smart city. Therefore, maximizing the benefits of technologies such as cloud computing, fog computing, and the Internet of things is important to manage and manipulate data in smart cities. However, certain types of data are sensitive and risky and may be infiltrated by malicious attacks. As a result, such data may be corrupted, thereby causing concern. The damage inflicted by an attacker on a set of data can spread through an entire database. Valid transactions that have read corrupted data can update other data items based on the values read. In this study, we introduce a unique model that uses fog computing in smart cities to manage utility service companies and consumer data. We also propose a novel technique to assess damage to data caused by an attack. Thus, original data can be recovered, and a database can be returned to its consistent state as no attacking has occurred.

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“…Dispite the cloud advantages, there are different obstacles in the cloud computing environment varying from long response times to security issues. As a solution to cloud computing performance related issues, e.g., high response time and real time data processing [1], CISCO introduced fog computing in 2012 , as multiple clouds are deployed close to the ground. This promising technology (i.e., fog computing) enables end users to utilize computing and processing services at the edge of the network [2], which in turn reduces the response time and the computation overhead to the cloud data centers.…”

Section: Introductionmentioning

confidence: 99%

“…Organizations adapt the fog computing paradigm by deploying multiple fog nodes to serve an organization's objectives. However, customers (i.e., individuals and organizations) are concerned about their data security against any malicious actors when they are process in the fog nodes [1]. Although adapting fog computing to intelligent systems would reduce load on the cloud and optimize the processing time and energy consumption as well [3].…”

Section: Introductionmentioning

confidence: 99%

Data Integrity Preservation Schemes in Smart Healthcare Systems That Use Fog Computing Distribution

et al. 2021

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The volume of data generated worldwide is rapidly growing. Cloud computing, fog computing, and the Internet of Things (IoT) technologies have been adapted to compute and process this high data volume. In coming years information technology will enable extensive developments in the field of healthcare and offer health care providers and patients broadened opportunities to enhance their healthcare experiences and services owing to heightened availability and enriched services through real-time data exchange. As promising as these technological innovations are, security issues such as data integrity and data consistency remain widely unaddressed. Therefore, it is important to engineer a solution to these issues. Developing a damage assessment and recovery control model for fog computing is critical. This paper proposes two models for using fog computing in healthcare: one for private fog computing distribution and one for public fog computing distribution. For each model, we propose a unique scheme to assess the damage caused by malicious attack, to accurately identify affected transactions and recover damaged data if needed. A transaction-dependency graph technique is used for both models to observe and monitor all transactions in the whole system. We conducted a simulation study to assess the applicability and efficacy of the proposed models. The evaluation rendered these models practicable and effective.

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Ensuring Data Integrity in Fog Computing Based Health-Care Systems

Cited by 20 publications

References 14 publications

Blockchain-Based Secure Healthcare Application for Diabetic-Cardio Disease Prediction in Fog Computing

Blockchain-Based Secure Healthcare Application for Diabetic-Cardio Disease Prediction in Fog Computing

Maintaining Data Integrity in Fog Computing Based Critical Infrastructure Systems

Data Integrity Preservation Schemes in Smart Healthcare Systems That Use Fog Computing Distribution

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