Robust Rank Reduction Algorithm with Iterative Parameter Optimization and Vector Perturbation

Jiao, Feng; Lamare, Rodrigo C. de

doi:10.3390/a8030573

Cited by 3 publications

(3 citation statements)

References 21 publications

(35 reference statements)

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“…In [18] explained edge recognition technique to find low-value data, to keep input data for distribution purpose. Paper [19] developed a low-complexity robust data-dependent dimensionality reduction model for reducedrank beam forming and steering vector estimation. In [20] designed K -anonymity method to achieve privacy in many data publishing applications.…”

Section: Related Workmentioning

confidence: 99%

A Credential Data Privacy Preserving in web Environment using Secure Data Contribution Retrieval Algorithm

Kumaran¹,

Khare²

2019

Journal of Intelligent Computing

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Preservation of privacy is a significant aspect of data mining and as the secrecy of sensitive information must be maintained while sharing the data among different untrusted parties. There are much application is suffering from vulnerable, data leakage, data misuse, and sensitive data disclosure issues. To protect the privacy of sensitive data without losing the usability of data, various techniques have been used in privacy-preserving data mining (PPDM). However, a system is unable to maintain the privacy during online services. Some of the approaches are available to maintain the tight privacy, but they fail to minimize the execution time and error rate. The main objective of the article is to contribute and retrieve the data with minimal classification error and execution time with enhanced privacy. To overcome the issues, the paper introduces the Secure Data Contribution Retrieval algorithm to fulfill the current issues. Proposed algorithms define a privacy policy and arrange the security based on requirements. This design applies the privacy based on the compatibility of applications. This approach computes the union of private multi-datasets that each of the interacting with attributes and actors and another that tests the inclusion of an element held by one actor in a subset of another. It displays the table with hidden attributes in multiple categories wise for a user. This approach is capable of satisfying the accuracy constraints for multiple datasets. It also considers the efficient data extraction with a good ranking of attributes in tables. Based on experimental result proposed approach performs well regarding success rate, error rate and system execution time compare than existing methods.

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Section: Related Workmentioning

confidence: 99%

A Credential Data Privacy Preserving in web Environment using Secure Data Contribution Retrieval Algorithm

Kumaran¹,

Khare²

2019

Journal of Intelligent Computing

View full text Add to dashboard Cite

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“…There are two typical identification methods for multivariate output-error systems: stochastic gradient (SG) algorithms [12,13] and the recursive least squares (RLS) algorithms [14,15]. The SG algorithm requires lower computational cost, but the RLS algorithm has a faster convergence rate than the SG algorithm [16].…”

Section: Introductionmentioning

confidence: 99%

Coupled Least Squares Identification Algorithms for Multivariate Output-Error Systems

Huang

Ding

2017

Algorithms

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This paper focuses on the recursive identification problems for a multivariate output-error system. By decomposing the system into several subsystems and by forming a coupled relationship between the parameter estimation vectors of the subsystems, two coupled auxiliary model based recursive least squares (RLS) algorithms are presented. Moreover, in contrast to the auxiliary model based recursive least squares algorithm, the proposed algorithms provide a reference to improve the identification accuracy of the multivariate output-error system. The simulation results confirm the effectiveness of the proposed algorithms.

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“…In [18] explained edge recognition technique to find low-value data, to keep input data for distribution purpose. In [19] developed a low-complexity robust data-dependent dimensionality reduction model for reduced-rank beam forming and steering vector estimation. In [20] designed K-anonymity method to achieve privacy in many data publishing applications.…”

Section: Related Workmentioning

confidence: 99%

A Credential Data Privacy Preserving in Web Environment Using Secure Data Contribution Retrieval Algorithm

Umapathy¹,

Khare²

2017

IJIES

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Abstract:Preservation of privacy is a significant aspect of data mining and as the secrecy of sensitive information must be maintained while sharing the data among different untrusted parties. There are many application is suffering from vulnerable, data leakage, data misuse, and sensitive data disclosure issues. To protect the privacy of sensitive data without losing the usability of data, various techniques have been used in privacy-preserving data mining (PPDM). Some of the approaches are available to maintain the tight privacy, but they fail to minimize the execution time and error rate. The main objective of the article is to contribute and retrieve the data with minimal classification error and execution time with enhanced privacy. To overcome the issues, the paper introduces the Secure Data Contribution Retrieval algorithm (SDCRA) to fulfill the current issues. Proposed algorithms define a privacy policy and arrange the security based on requirements. This design applies the privacy based on the compatibility of applications. This approach is capable of satisfying the accuracy constraints for multiple datasets. It also considers the efficient data extraction with a good ranking of attributes in tables. Here, proposed SDCRA is compared with existing approaches namely as Perturbation, singular value decomposition (SVD), Singular Value Decomposition data Perturbation (SVD+DP), K-anonymity with Decision Tree (KA+DT)[] for Cancer, HIV, Diabetes dataset. Based on experimental result proposed approach performs well regarding success rate, error rate and system execution time compare than existing methods. Proposed approach improves Success Rate 1.83% reduces the Error Rate 2.33% and minimizes the system execution time 2 seconds.

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Robust Rank Reduction Algorithm with Iterative Parameter Optimization and Vector Perturbation

Cited by 3 publications

References 21 publications

A Credential Data Privacy Preserving in web Environment using Secure Data Contribution Retrieval Algorithm

A Credential Data Privacy Preserving in web Environment using Secure Data Contribution Retrieval Algorithm

Coupled Least Squares Identification Algorithms for Multivariate Output-Error Systems

A Credential Data Privacy Preserving in Web Environment Using Secure Data Contribution Retrieval Algorithm

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