An efficient perturbation approach for multivariate data in sensitive and reliable data mining-Reference-Cited by-同舟云学术

An efficient perturbation approach for multivariate data in sensitive and reliable data mining

Published:2021-11 Issue: Volume:62 Page:102954
ISSN:2214-2126
Container-title:Journal of Information Security and Applications
language:en
Short-container-title:Journal of Information Security and Applications

Author:

Paul Mahit Kumar,Islam Md. Rabiul,Sattar A.H.M. Sarowar

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality,Software

Reference46 articles.

1. Efficient data perturbation for privacy preserving and accurate data stream mining;Chamikara;Pervasive Mob Comput,2018

2. Respecting data privacy in educational data mining: An approach to the transparent handling of student data and dealing with the resulting missing value problem;Askinadze,2018

3. Enhancing random projection with independent and cumulative additive noise for privacy-preserving data stream mining;Denham;Expert Syst Appl,2020

4. Mining in educational data: Review and future directions;Salloum,2020

5. Saving human lives: What complexity science and information systems can contribute;Helbing;J Stat Phys,2015

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. CARBO: Clustering and rotation based oversampling for class imbalance learning;Knowledge-Based Systems;2024-09

2. Image-based random rotation for preserving the data in data mining process;Signal, Image and Video Processing;2024-03-19

3. Quantile-constrained Wasserstein projections for robust interpretability of numerical and machine learning models;Electronic Journal of Statistics;2024-01-01

4. Efficient perturbation techniques for preserving privacy of multivariate sensitive data;Array;2023-12

5. Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review;Artificial Intelligence Review;2023-02-22