A hybrid-security model for privacy-enhanced distributed data mining

Javid, Tanzeela; Gupta, Manoj Kumar; Gupta, Abhishek

doi:10.1016/j.jksuci.2020.06.010

Cited by 12 publications

(12 citation statements)

References 39 publications

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“…In [17], implementing the hybrid security model improves privacy in DDM. The introduced method uses the k-means clustering and na ve Bayes classification method to ensure the two levels of security.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…To overcome this issue, privacy protection-based distributed clustering (PPDC) with optimized deep learning approaches is included in this work. The introduced PPDC-ODL method performance is compared with the existing researcher's works such as [16,17,21,22]. Among the several methods, these methods are chosen because of effective guidelines and methods utilized to preserve the sensitive data in the distributed environment.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Finally, the selected solution was investigated using a genetic operator, which minimizes the binary optimization problem of the network parameter updating process (17):…”

Section: J X Y I I K Ii Kmentioning

confidence: 99%

“…The generated local and global code effectiveness is evaluated using the CAI metric. The obtained results are compared with the existing researcher's techniques such as distributed perturbation algorithm (DISTPAB) [16], k-means clustering and na ve Bayes classification (K-MC-NB) [17], blockchain-based privacy algorithm in a distributed database (DEPLSEST) [21] and multi-core DBSCAN [22]. Then the obtained results are illustrated in Table 2.…”

Section: Consistent Accuracy Index (Cai)mentioning

confidence: 99%

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Privacy protection based distributed clustering with deep learning algorithm for distributed data mining

Mahmood¹,

Naser²,

Khalil

2022

EEJET

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Distributed Data Mining (DDM) is vital in various applications for processing large volumes of data. The datasets are saved in the local databases and operated by local communities, but it provides the solution locally and globally. However, the datasets are stored in a distributed manner which affects the scalability and reliability issues. In addition, locally stored data is influenced by security and privacy challenges. In addition, the third party may access the DDM, which causes authorization issues. Therefore, the DDM process fuses sensor data from different sources to improve knowledge discovery. During this process, the DDM faces several issues such as security concerns, privacy restrictions, technical barriers, and trust issues. To address these issues, distributed data mining (DDM) should be improved to handle homogeneous and heterogeneous data. This work uses the privacy protection-based distributed clustering (PPDC) algorithm to handle the privacy and security challenges while analyzing the distributed data. The clustering algorithm generates the semi-trusted third parties to form the cluster, which protects the data from unauthorized users. The semi-trusted party protect the locally analyzed solution by creating the random vector-based trusted process. Further, the process uses the optimized deep learning approach and clustering to improve the heterogeneous data analysis. Then the effectiveness of the introduced PPDC method is compared with existing methods, and the PPDC algorithm ensures the 0.202 error rate, 0.95 % of accuracy and manages the data security.

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Finally, the selected solution was investigated using a genetic operator, which minimizes the binary optimization problem of the network parameter updating process (17):…”

Section: J X Y I I K Ii Kmentioning

confidence: 99%

Section: Consistent Accuracy Index (Cai)mentioning

confidence: 99%

See 2 more Smart Citations

Privacy protection based distributed clustering with deep learning algorithm for distributed data mining

Mahmood¹,

Naser²,

Khalil

2022

EEJET

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“…Data mining systems can violate privacy. Absence of safety and security can be very detrimental to its users and it can create miscommunication between employees, thus leading to genuine privacy concerns [177].…”

Section: Limitations and Challengesmentioning

confidence: 99%

A Review of Data Mining Applications in Semiconductor Manufacturing

Espadinha-Cruz

Godina

Rodrigues³

2021

Processes

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For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.

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A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images

Trivedi

Gupta

2021

Multimed Tools Appl

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Pneumonia is a life-threatening respiratory lung disease. Children are more prone to be affected by the disease and accurate manual detection is not easy. Generally, chest radiographs are used for the manual detection of pneumonia and expert radiologists are required for the assessment of the X-ray images. An automatic system would be beneficial for the diagnosis of pneumonia based on chest radiographs as manual detection is time-consuming and tedious. Therefore, a method is proposed in this paper for the fast and automatic detection of pneumonia. A deep learning-based architecture ‘MobileNet’ is proposed for the automatic detection of pneumonia based on the chest X-ray images. A benchmark dataset of 5856 chest X-ray images was taken for the training, testing, and evaluation of the proposed deep learning network. The proposed model was trained within 3 Hrs. and achieved a training accuracy of 97.34%, a validation accuracy of 87.5%, and a testing accuracy of 94.23% for automatic detection of pneumonia. However, the combined accuracy was achieved as 97.09% with 0.96 specificity, 0.97 precision, 0.98 recall, and 0.97 F-Score. The proposed method was found faster and computationally lesser expensive as compared to other methods in the literature and achieved a promising accuracy.

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A hybrid-security model for privacy-enhanced distributed data mining

Cited by 12 publications

References 39 publications

Privacy protection based distributed clustering with deep learning algorithm for distributed data mining

Privacy protection based distributed clustering with deep learning algorithm for distributed data mining

A Review of Data Mining Applications in Semiconductor Manufacturing

A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images

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