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Updated: Mar 30, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

A comprehensive review on privacy preserving data mining.

Yousra Abdul Alsahib S Aldeen¹, Mazleena Salleh², Mohammad Abdur Razzaque²

¹Faculty of Computing, University Technology Malaysia, UTM, 81310 UTM Skudai, Johor Malaysia ; Department of Computer Science, College of Education, Ibn Rushd, Baghdad University, Baghdad, Iraq.

|November 21, 2015

Summary

This summary is machine-generated.

Protecting privacy in data mining is crucial due to internet phishing threats. This review categorizes current privacy-preserving data mining techniques, highlighting their pros, cons, and future research needs.

Keywords:

Association Classification Clustering Data mining Distortion K-anonymity Outsourcing Privacy preserving

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

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Area of Science:

Computer Science
Information Security

Background:

Data privacy is essential for secure information exchange, analysis, and publication.
Internet phishing and data disclosure concerns create significant barriers to data sharing.
Existing methods often fail to adequately protect sensitive information, leading to mistrust.

Purpose of the Study:

To provide a comprehensive overview of privacy-preserving data mining (PPDM) techniques.
To systematically interpret and organize existing literature on PPDM.
To identify current challenges and future research directions in the field.

Main Methods:

Categorization of PPDM techniques based on distortion, association rules, taxonomy, clustering, associative classification, outsourced data mining, distributed methods, and k-anonymity.
Analysis of the merits and shortcomings of each classified technique.
Systematic review and interpretation of published literature.

Main Results:

PPDM techniques are diverse, including distortion-based, rule-based, clustering, and k-anonymity approaches.
Each method presents unique advantages and disadvantages regarding privacy protection and data utility.
Significant gaps and weaknesses exist in current PPDM strategies.

Conclusions:

Robust privacy protection in data mining requires continuous enhancement and development.
Future research must address the identified weaknesses to ensure data security and trustworthiness.
Further advancements are mandatory for more resilient privacy preservation in data mining applications.