A Fuzzy based Data Perturbation Technique for Privacy Preserved Data Mining

Shynu, P G; Shayan., H Md.; Chowdhary, Chiranji Lal

doi:10.1109/ic-etite47903.2020.244

Cited by 15 publications

(8 citation statements)

References 9 publications

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“…In order to strengthen the possibilistic c-mean algorithm, we have hybridization with an intuitionist fuzzy c-mean algorithm. The Intuitionist fuzzy c-mean algorithm used to solve uncertainty issues by addressing degree of hesitation during the membership function [ 51 , 52 , 53 ].…”

Section: Methodsmentioning

confidence: 99%

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

Chowdhary

Mittal

Kumaresan

et al. 2020

Sensors

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The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.

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Section: Methodsmentioning

confidence: 99%

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

Chowdhary

Mittal

Kumaresan

et al. 2020

Sensors

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“…This work may consider the various images of varying sizes in pixels. P. G. Shynu et al [1] proposed a fuzzy-based data transformation technique to preserve privacy by using privacy-preserving data mining (PPDM) in the database. Privacy-preserving data mining is used to transform fuzzy data original dataset.…”

Section: Related Workmentioning

confidence: 99%

“…The light has a photon; it carries a rigid amount of energy and polarization physical property. Polarization node is classified into two types [1]. One is a rectilinear polarization node; another one is the diagonal polarization node.…”

Section: Introductionmentioning

confidence: 99%

Enhanced BB84 quantum cryptography protocol for secure communication in wireless body sensor networks for medical applications

Devi¹,

Kalaivani²

2021

Pers Ubiquit Comput

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Wireless body sensor network (WBSN) is an interdisciplinary field that could permit continuous health monitoring with constant clinical records updates through the Internet. WBAN is a special category of wireless networks. Coronavirus disease 2019 (COVID-19) pandemic creates the situation to monitor the patient remotely following the social distance. WBSN provides the way to effectively monitor the patient remotely with social distance. The data transmitted in WBSN are vulnerable to attacks and this is necessary to take security procedure like cryptographic protocol to protect the user data from attackers. Several physiological sensors are implanted in the human body that will collect various physiological updates to monitor the patient's healthcare data remotely. The sensed information will be transmitted wirelessly to doctors all over the world. But it has too many security threats like data loss, masquerade attacks, secret key distribution problems, unauthorized access, and data confidentiality loss. When any attackers are attacking the physiological sensor data, there is a possibility of losing the patient's information. The creation, cancellation, and clinical data adjustment will produce a mass effect on the healthcare monitoring system. Present-day cryptographic calculations are highly resistant to attacks, but the only weak point is the insecure movement of keys. In this paper, we look into critical security threats: secure key distribution. While sharing the secret key between communicating parties in the wireless body sensor networks in the conventional method like via phone or email, the attackers will catch the private key. They can decrypt and modify more sensitive medical data. It can cause a significant effect like death also. So need an effective, secure key distribution scheme for transmission of human body health related data to medical professional through wireless links. Moreover, a new enhanced BB84 Quantum cryptography protocol is proposed in this paper for sharing the secret key among communicating parties in a secure manner using quantum theory. Besides, a bitwise operator is combined with quantum concepts to secure the patient's sensed information in the wireless environment. Instead of mail and phone via sharing secret key, quantum theory with the bitwise operator is used here. Therefore, it is not possible to hack the secret key of communication. The body sensor's constrained assets as far as battery life, memory, and computational limit are considered for showing the efficiency of the proposed security framework. Based on experimental results, it is proven that the proposed algorithm EBB84QCP provides high secure key distribution method without direct sharing the secret key and it used the quantum mechanism and bitwise operator for generating and distributing secret key value to communicating parties for sensitive information sharing in the wireless body sensor networks.

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“…In order to disorder the rectangular blocks after the image was segmented, rectangle blocks scrambling algorithm is used to reorganize the segmented image [14]- [15]. A set of random non-repeated sequence are created according to logistic iterative formula.…”

Section: B Rectangle Blocks Scramblingmentioning

confidence: 99%

Implementation of Fuzzy C-Means (FCM) Clustering Based Camouflage Image Generation Algorithm

et al. 2021

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Camouflage plays a fundamental role in modern electrical confrontation. Two important elements of camouflage are camouflage colors and camouflage textures. Many methods were presented to extract the main colors of the background. However, there are few methods to extract the background textures at present. The traditional methods based on watershed segmentation or background contour segmentation are computationally complex and time-consuming, being difficult to meet the real-time requirements. In this paper, a camouflage generation algorithm based on rectangle blocks scrambling and Fuzzy C-Means (FCM) clustering method is proposed. The algorithm consists of three modules, namely (1) the rectangle blocks segmentation module, (2) the rectangle scrambling module, and (3) the extraction of background dominant colors. Firstly, the texture features of the background image are simulated by rectangle blocks segmentation and scrambling algorithm, which avoids the complex calculation process and the loss of textures information compared with traditional algorithms based on description operators for background textures extraction. Next, Fuzzy C-Means(FCM) method is used to quickly extract the main colors of background image with high accuracy. In addition, experiments show that the proposed algorithm reduces the computing time and presents better concealment effect by retaining the similar domain colors with FCM clustering and background textures with blocks scrambling method. Compared with the template traversal algorithm and the watershed segmentation algorithm, the proposed algorithm features reduced computing time by more than 50%, and an increased similarity between the generated texture and background texture to more than 90%.

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A Fuzzy based Data Perturbation Technique for Privacy Preserved Data Mining

Cited by 15 publications

References 9 publications

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

Enhanced BB84 quantum cryptography protocol for secure communication in wireless body sensor networks for medical applications

Implementation of Fuzzy C-Means (FCM) Clustering Based Camouflage Image Generation Algorithm

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