Data Mining-Based Privacy Preservation Technique for Medical Dataset Over Horizontal Partitioned

Mewada, Shivlal

doi:10.4018/ijehmc.20210901.oa4

Cited by 6 publications

(4 citation statements)

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“…[22], on the other hand, it does not provide an explicit process for reconstructing the actual data values. [23][24][25] have looked at the concept in the framework of mining techniques and made it appropriate for minimizing privacy violations. Our significant contribution is to present a straightforward filtering approach based on privacy enhancement in data mining using arbitrariness and perturbation for estimating the actual data values.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Murugeshwari¹,

Rajalakshmi²,

Sudharson³

2023

Computer Systems Science and Engineering

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Imagine numerous clients, each with personal data; individual inputs are severely corrupt, and a server only concerns the collective, statistically essential facets of this data. In several data mining methods, privacy has become highly critical. As a result, various privacy-preserving data analysis technologies have emerged. Hence, we use the randomization process to reconstruct composite data attributes accurately. Also, we use privacy measures to estimate how much deception is required to guarantee privacy. There are several viable privacy protections; however, determining which one is the best is still a work in progress. This paper discusses the difficulty of measuring privacy while also offering numerous random sampling procedures and statistical and categorized data results. Furthermore, this paper investigates the use of arbitrary nature with perturbations in privacy preservation. According to the research, arbitrary objects (most notably random matrices) have "predicted" frequency patterns. It shows how to recover crucial information from a sample damaged by a random number using an arbitrary lattice spectral selection strategy. This filtration system's conceptual framework posits, and extensive practical findings indicate that sparse data distortions preserve relatively modest privacy protection in various situations. As a result, the research framework is efficient and effective in maintaining data privacy and security.

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Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Murugeshwari¹,

Rajalakshmi²,

Sudharson³

2023

Computer Systems Science and Engineering

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“…To benefit from the advantages of multiple cloud deploy- ment methodologies, a hybrid cloud concept is introduced which integrates two or more clouds. Private virtual cloud is the term used to describe the common pool of resources in a cloud system [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Improved Association Rule Mining-Based Data Sanitization for Privacy Preservation Model in Cloud

Patil¹,

HimaBindu²

2023

JTIT

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Data security in cloud services is achieved by imposing a broad range of privacy settings and restrictions. However, the different security techniques used fail to eliminate the hazard of serious data leakage, information loss and other vulnerabilities. Therefore, better security policy requirements are necessary to ensure acceptable data protection levels in the cloud. The two procedures presented in this paper are intended to build a new cloud data security method. Here, sensitive data stored in big datasets is protected from abuse via the data sanitization procedure relying on an improved apriori approach to clean the data. The main objective in this case is to generate a key using an optimization technique known as Corona-integrated Archimedes Optimization with Tent Map Estimation (CIAO-TME). Such a technique deals with both restoration and sanitization of data. The problem of optimizing the data preservation ratio (IPR), the hiding ratio (HR), and the degree of modification (DOM) is formulated and researched as well.

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“…Xu et al [9] proposed two privacy-preserving schemes by adding Laplace noise to the original rating and user similarity metric processes, respectively. Mewada [10] proposed a DP-preserving neighbor-based CF algorithm for the privacy leakage problem faced by the k-nearest neighbor algorithm. For label-based recommendation systems, Wang et al [11] proposed a DP-preserving algorithm for modifying and publishing user profles.…”

Section: Introductionmentioning

confidence: 99%

A Normative Approach to Privacy-Preserving Recommender Systems: Integrating Matrix Factorization and Genetic Algorithms

He,

2023

International Journal of Intelligent Systems

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As recommendation systems heavily depend on user data, these systems are susceptible to potential privacy breaches. To mitigate this issue, differential privacy (DP) protection techniques have been developed to offer robust privacy safeguards. Nevertheless, a majority of the extant DP-based recommendation algorithms tend to introduce excessive noise, consequently impairing the quality of recommendations. In response, this study presents a novel DP-preserving recommendation algorithm that integrates matrix factorization (MF) and a genetic algorithm (GA). Initially, the MF problem is transformed into two interrelated optimization problems, namely, the user-hidden factor and the item-hidden factor. Subsequently, GA is employed to address these optimization issues. An enhancement index mechanism is incorporated into the individual selection of GA, while the variation process of GA is devised based on identifying crucial hidden factors. Utilizing the enhancement index mechanism aids in minimizing the algorithm’s perturbation level, thereby achieving an optimal balance between privacy protection and algorithm utility. Experimental analyses, encompassing recommendation accuracy, efficiency, and parameter variation effects, are conducted on Last.fm and Flixster datasets. The findings corroborate that the proposed system outperforms existing alternatives under stringent privacy constraints, thereby attesting to its efficacy.

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Data Mining-Based Privacy Preservation Technique for Medical Dataset Over Horizontal Partitioned

Cited by 6 publications

References 50 publications

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Improved Association Rule Mining-Based Data Sanitization for Privacy Preservation Model in Cloud

A Normative Approach to Privacy-Preserving Recommender Systems: Integrating Matrix Factorization and Genetic Algorithms

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