A Secure Data Classification Model in Cloud Computing Using Machine Learning Approach

Kaur, Kulwinder; Zandu, Vikas

doi:10.14257/ijgdc.2016.9.8.02

Cited by 13 publications

(7 citation statements)

References 9 publications

(6 reference statements)

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“…Related work has been done in cloud computing 43‐47 but not from the form field level. Instead, sensitive files and documents are identified and encrypted or stored in different areas accordingly.…”

Section: Discussionmentioning

confidence: 99%

“…For other form fields, context during the data collection process plays a role in determining the ultimate sensitivity level of these fields. Related work has been done in cloud computing [43][44][45][46][47] but not from the form field level. Instead, sensitive files and documents are identified and encrypted or stored in different areas accordingly.…”

Section: Discussionmentioning

confidence: 99%

“…Instead, sensitive files and documents are identified and encrypted or stored in different areas accordingly. Other authors [43][44][45] have used machine learning particularly classification algorithms to classify the data into different sensitivity levels. In such scenarios, domain experts help define the initial training data sets.…”

Section: Discussionmentioning

confidence: 99%

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Form‐based security in mobile health data collection systems

Katarahweire

Mughal

Ngubiri

2021

Security and Privacy

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Mobile Health Data Collection Systems (MHDCS) use electronic forms for data collection in place of paper surveys. Data is entered into the electronic forms by community health workers with smartphones. These systems are used in clinical trials and continuous monitoring of participants. Data collected in MHDCS is diverse with varying sensitivity levels. This therefore calls for the application of different security mechanisms to protect the data. In this paper, a data sensitivity classification model is proposed. It determines the sensitivity levels of form attributes depending on the context and sensitive parameters. The electronic form is the starting point of the data collection process. We augment the form with sensitivity levels and a mapping of security mechanisms to the sensitivity levels is made. Through threat modeling, we identify potential threats and possible mitigations. We demonstrate the feasibility of our approach by prototyping the proposed model into the Open Data Kit tool suite. Tests show that the security mitigations specified during form design are executed once the secure form is loaded on the mobile device during data collection. Our ultimate aim is to facilitate the addition of security requirements into the data collection processes right from the start of the design process.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Form‐based security in mobile health data collection systems

Katarahweire

Mughal

Ngubiri

2021

Security and Privacy

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“…Kaur and Zandu proposed a method with two levels of computing, namely authentication and storage, to analyze the security issues in the cloud computing environment. In order to facilitate decision making for data classification, several constraints are tobe taken into account, and different techniques are mandatory to enhance the efficiency of classification [15].Farooq and Hussa in presented a hybrid CDSS which consists of two parts:(i) ontology-driven clinical risk assessment and recommendation system, and (ii) machine learning-driven prognostic system of cardiovascular diseases. The proposed CDSS can manage medical errors in the risk assessment process.…”

Section: Related Workmentioning

confidence: 99%

Privacy Protected Medical Data Classification in precision medicine using an Ontology-based Support Vector Machine in the Diabetes Management System

2019

IJITEE

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Diabetes is a serious health issue across the globe. As stated by the International Diabetes Foundation, currently 425 million people live with diabetes globally, and another 300 million people are expecting to be at higher risk of diabetes in the year 2030.Hence, there is an urgent clinical need for an early prognosis, diagnosis, and management of diabetes and its complications. In this context, numerous intelligent machine learning and data mining approaches including Support vector machine (SVM) have been exploited for diabetes management.SVM is a prevailing supervised and discriminative data classifier which assists the healthcare and biomedical professionals to ascertain unknown data patterns by training a large volume of real-time data. Since this database is the private asset, the sensitive information should be safeguarded without compromising the utility. Even though SVM is a fast and accurate machine learning technique, it does not have the capacity to represent semantically the classification and reasoning rules which can enable more precise classification. Therefore, the objective of this research is three-fold. Firstly, in order to protect sensitive clinical data, we introduce the Kronecker product and Crow Search Algorithm (CSA) based coefficient generation technique. Secondly, we design diabetes ontology to define domain concepts and relationships and allow medical datamining. Finally, we implement the Ontology-based Support Vector Machine (Ont-SVM) classifier by assimilating privacy protection, ontology, and SVM-based classifier for the diagnosis of diabetes.The experimental results on a real-world dataset, the Pima Indian database at the UCI repository, illustrate that the proposed Ont-SVM outperforms other existing approaches in terms of privacy, accuracy, specificity, and sensitivity.

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“…Kaur and Zandu presented an approach to study the security problems in the cloud at 2 levels of computing, authentication, and storage. To make an effective decision regarding classification, more security parameters are to be considered, and various mechanisms are required to improve the performance.…”

Section: Motivationmentioning

confidence: 99%

OW‐SVM: Ontology and whale optimization‐based support vector machine for privacy‐preserved medical data classification in cloud

Karlekar

Gomathi

2018

Int J Communication

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Summary Cloud is a multitenant architecture that allows the cloud users to share the resources via servers and is used in various applications, including data classification. Data classification is a widely used data mining technique for big data analysis. It helps the learners to discover hidden data patterns by training massive data collected from the real world. Because this trained model is the private asset of an entity, it should be protected from all other noncollaborative entities. Therefore, it is essential to take effective measures to preserve the confidential data. The objective of this paper is to preserve the privacy of the confidential data in the cloud environment by introducing the medical data classification method. In view of that, this paper presents a method for medical data classification using a novel ontology and whale optimization‐based support vector machine (OW‐SVM) approach. Initially, privacy‐preserved data are developed adopting Kronecker product bat approach, and then, ontology is built for the feature selection process. Ontology and whale optimization‐based support vector machine is then proposed by integrating ontology and whale optimization algorithm into SVM, in which ontology and whale optimization algorithm is used for the feasible selection of kernel parameters. The experiment is done using 3 heart disease datasets, such as Cleveland, Switzerland, and Hungarian. In a comparative analysis, the performance of the OW‐SVM approach is compared with that of K‐nearest neighbor, Naive Bayes, decision tree, SVM, and OW‐SVM, using accuracy, sensitivity, specificity, and fitness, as the evaluation metrics. The OW‐SVM approach could achieve maximum performance with accuracy of 83.21%, the sensitivity of 91.49%, specificity of 73%, and fitness of 81.955, outperforming existing comparative techniques.

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A Secure Data Classification Model in Cloud Computing Using Machine Learning Approach

Abstract: Abstract

Cited by 13 publications

References 9 publications

Form‐based security in mobile health data collection systems

Form‐based security in mobile health data collection systems

Privacy Protected Medical Data Classification in precision medicine using an Ontology-based Support Vector Machine in the Diabetes Management System

OW‐SVM: Ontology and whale optimization‐based support vector machine for privacy‐preserved medical data classification in cloud

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