An Efficient KNN Classification by using Combination of Additive and Multiplicative Data Perturbation for Privacy Preserving Data Mining

2015 ◽  
Vol 22 (7) ◽  
pp. 290-295
Author(s):  
Bhupendra Kumar Pandya ◽  
◽  
Umesh kumar Singh ◽  
Keerti Dixit
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Data Perturbation ◽  
Privacy Preserving Data Mining ◽  
Knn Classification

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 Effective Gene Patterned Association Rule Hiding Algorithm for Privacy Preserving Data Mining on Transactional Database

2017 ◽  
Vol 17 (3) ◽  
pp. 92-108 ◽  
Author(s):  
P. Gayathiri ◽  
B. Poorna
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Gene Selection ◽  
Perturbation Technique ◽  
Privacy Preserving ◽  
Data Perturbation ◽  
Data Mining Technique ◽  
Privacy Preserving Data Mining ◽  
Taxi Service

Abstract Association Rule Hiding methodology is a privacy preserving data mining technique that sanitizes the original database by hide sensitive association rules generated from the transactional database. The side effect of association rules hiding technique is to hide certain rules that are not sensitive, failing to hide certain sensitive rules and generating false rules in the resulted database. This affects the privacy of the data and the utility of data mining results. In this paper, a method called Gene Patterned Association Rule Hiding (GPARH) is proposed for preserving privacy of the data and maintaining the data utility, based on data perturbation technique. Using gene selection operation, privacy linked hidden and exposed data items are mapped to the vector data items, thereby obtaining gene based data item. The performance of proposed GPARH is evaluated in terms of metrics such as number of sensitive rules generated, true positive privacy rate and execution time for selecting the sensitive rules by using Abalone and Taxi Service Trajectory datasets.

Data Perturbation Method Based on Contrast Mapping for Reversible Privacy-preserving Data Mining

2015 ◽  
Vol 35 (6) ◽  
pp. 789-794 ◽  
Author(s):  
Yuan-Hung Kao ◽  
Wei-Bin Lee ◽  
Tien-Yu Hsu ◽  
Chen-Yi Lin ◽  
Hui-Fang Tsai ◽  
...  
Keyword(s):  
Data Mining ◽  
Perturbation Method ◽  
Privacy Preserving ◽  
Data Perturbation ◽  
Privacy Preserving Data Mining
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