A Combined Clustering and Geometric Data Perturbation Approach for Enriching Privacy Preservation of Healthcare Data in Hybrid Clouds

Vulapula, Sridhar Reddy; Rao, B. Thirumala

doi:10.22266/ijies2018.0228.21

Cited by 16 publications

(8 citation statements)

References 27 publications

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“…R, , and are the multiplicative, translation, and additive matrices respectively. The integration of these sub-transformations shows well utility and privacy guarantees during computations [106], [107].…”

Section: Perturbation-based Approachesmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

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With the ever-increasing requirement of storage and computation resources, it is unrealistic for local devices (with limited sources) to implement large-scale data processing. Therefore, individuals or corporations incline to outsource their computation requirements to the cloud. However, data outsourcing brings security and privacy concerns to users when the cloud servers are not fully trusted. Recently, extensive research works are conducted, aiming at secure outsourcing schemes for diverse computational tasks via different technologies. In this survey, we provide a technical review and comparison of existing outsourcing schemes using diverse secure computation methods. Specifically, we begin the survey by describing security threats and requirements of secure outsourcing computation. Meanwhile, we introduce four secure techniques (i.e., secure multi-party computation, pseudorandom functions, software guard extensions, and perturbation approaches) and their related works. Then, we focus on the theories and evolution of homomorphic encryption, as well as the applications of the basic operations and application-specific tasks. Finally, we discuss the security and performance of existing works and give future directions in this field. INDEX TERMS Secure outsourced computing, privacy preserving, homomorphic encryption, secure outsourced machine learning, data processing.

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Section: Perturbation-based Approachesmentioning

confidence: 99%

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

et al. 2019

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“…In a different study [36], the data set containing personal health records was developed using the Gaussian noise model in geometric perturbation and compared with the AES encryption method in terms of operating times. In a similar study, geometric perturbation was performed using the Gaussian noise model, and healthcare data set [37]. The authors in [38] developed geometric data perturbation and a classification model accordingly.…”

Section: Literature Reviewsmentioning

confidence: 99%

“…When all these studies examined, geometric data perturbation models become special with step order or noise addition step. In most of the studies, it is seen that Gaussian noise model is a frequently used because of its ability to produce different noise each time [35][36][37][38][39][40]. And another one uses the Laplace noise [44].…”

Section: Literature Reviewsmentioning

confidence: 99%

A New Geometric Data Perturbation Method for Data Anonymization Based on Random Number Generators

Kanmaz

Aydın

Sertbaş

2021

JWE

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With the technology’s rapid development and its involvement in all areas of our lives, the volume and value of data have become a significant field of study. Valuation of the data to this extent has produced some consequences in terms of people’s knowledge. Data anonymization is the most important of these issues in terms of the security of personal data. Much work has been done in this area and continues to being done. In this study, we proposed a method called RSUGP for the anonymization of sensitive attributes. A new noise model based on random number generators has been proposed instead of the Gaussian noise or random noise methods, which are being used conventionally in geometric data perturbation. We tested our proposed RSUGP method with six different databases and four different classification methods for classification accuracy and attack resistance; then, we presented the results section. Experiments show that the proposed method was more successful than the other two classification accuracy, attack resistance, and runtime.

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“…Fuzzy miner is a new process discovery algorithm [23]. This is used to build design [24] unstructured process and nonbehavioral conflict events too.…”

Section: Fuzzy Miner Algorithmmentioning

confidence: 99%

Process Mining in Healthcare Systems: A Critical Review and its Future

2020

IJETER

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Nowadays, process mining is the emerging technique and is widely used across industry domains. Process mining is evolving important role for patients as well as hospital management. In this paper, we applied process mining techniques in health care industry. We used the process mining techniques to find the flow of process in different categories of users involving in processes. This process mining is used to provide an accurate solution of the care flows under each consideration. Our study explores various journals and conferences about the usage of process mining in dynamic health care systems using different algorithms and finds the patient's process flows and effectiveness of employees in hospital. The process flow extracts the information from the event log and visualizes the care flow with respect to different attributes and improves the work flow of process. This survey aims to study the various research papers about process mining using different techniques in health care and analyses gathering the findings.

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A Combined Clustering and Geometric Data Perturbation Approach for Enriching Privacy Preservation of Healthcare Data in Hybrid Clouds

Cited by 16 publications

References 27 publications

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

A Comprehensive Survey on Secure Outsourced Computation and Its Applications

A New Geometric Data Perturbation Method for Data Anonymization Based on Random Number Generators

Process Mining in Healthcare Systems: A Critical Review and its Future

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