Evaluating applicability of perturbation techniques for privacy preserving data mining by descriptive statistics

2016 ◽  
Author(s):  
Alpa Shah ◽  
Ravi Gulati
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Descriptive Statistics ◽  
Perturbation Techniques ◽  
Privacy Preserving Data Mining

Random-data perturbation techniques and privacy-preserving data mining

2005 ◽  
Vol 7 (4) ◽  
pp. 387-414 ◽  
Author(s):  
Hillol Kargupta ◽  
Souptik Datta ◽  
Qi Wang ◽  
Krishnamoorthy Sivakumar
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Perturbation Techniques ◽  
Data Perturbation ◽  
Random Data ◽  
Privacy Preserving Data Mining

scholarly journals Comparative Study on Perturbation Techniques in Privacy Preserving Data Mining on Two Numeric Datasets

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Desmond Ko Khang Siang ◽  
Siti Hajar Othman ◽  
Raja Zahilah Raja Mohd Radzi
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Naive Bayes ◽  
Perturbation Technique ◽  
Original Data ◽  
Privacy Preserving ◽  
Naïve Bayes ◽  
Support Vector ◽  
Perturbation Techniques ◽  
Privacy Preserving Data Mining

Data Mining is a computational process that able to identify patterns, trends and behaviour from large datasets. With this advantages, data mining has been applied in many fields such as finance, healthcare, retail and so on. However, information disclosure become one of an issue during data mining process. Therefore, privacy protection is needed during data mining process which known as Privacy Preserving Data Mining (PPDM). There are several techniques available in PPDM and each of the techniques has its’ own benefits and drawbacks. In this research, perturbation technique is selected as privacy preserving technique. Perturbation technique is a method that alters the original data value before the application of data mining. In PPDM applications, perturbation technique able to provide a protection of data privacy but the accuracy of data should not be ignored too. In this research, three perturbation techniques are selected which are additive noise, data swapping and resample. For data mining techniques, two methods of classification are selected which are Naïve Bayes and Support Vector Machines (SVM). With the selection of these techniques, the experimental results are evaluated based on the hiding failure, accuracy and precision. For overall result, resample is selected as the best perturbation technique in naïve bayes and SVM classification for both glass and ionosphere datasets.

A Perturbation Method Based on Singular Value Decomposition and Feature Selection for Privacy Preserving Data Mining

2014 ◽  
Vol 10 (1) ◽  
pp. 55-76 ◽  
Author(s):  
Mohammad Reza Keyvanpour ◽  
Somayyeh Seifi Moradi
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Singular Value Decomposition ◽  
Perturbation Method ◽  
Privacy Preserving ◽  
Singular Value ◽  
Privacy Preserving Data Mining ◽  
Selection For ◽  
Value Decomposition ◽  
Different Levels

In this study, a new model is provided for customized privacy in privacy preserving data mining in which the data owners define different levels for privacy for different features. Additionally, in order to improve perturbation methods, a method combined of singular value decomposition (SVD) and feature selection methods is defined so as to benefit from the advantages of both domains. Also, to assess the amount of distortion created by the proposed perturbation method, new distortion criteria are defined in which the amount of created distortion in the process of feature selection is considered based on the value of privacy in each feature. Different tests and results analysis show that offered method based on this model compared to previous approaches, caused the improved privacy, accuracy of mining results and efficiency of privacy preserving data mining systems.

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