scholarly journals PRIVACY PRESERVING DATA MINING USING RANDOM ROTATION BASED DATA PERTURBATION TECHNIQUE

2015 ◽  
Vol 2 (05) ◽  
Keyword(s):  
Data Mining ◽  
Perturbation Technique ◽  
Privacy Preserving ◽  
Data Perturbation ◽  
Privacy Preserving Data Mining ◽  
Random Rotation

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.

scholarly journals DATA PROCESSING THROUGH AN ADDITIVE ROTATIONAL PERTURBATION TECHNIQUE IN A SECURED ENVIRONMENT OF PPRIVACY

2021 ◽  
Vol 9 (2) ◽  
pp. 131-135
Author(s):  
G. Srinivas Reddy, Et. al.
Keyword(s):  
Data Mining ◽  
Privacy Preservation ◽  
Web Applications ◽  
Perturbation Technique ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Experimental Result ◽  
Perturbation Approach ◽  
Privacy Preserving Data Mining ◽  
Rotational Perturbation

As the usage of internet and web applications emerges faster, security and privacy of the data is the most challenging issue which we are facing, leading to the possibility of being easily damaged. Various conventional techniques are used for privacy preservation like condensation, randomization and tree structure etc., the limitations of the existing approaches are, they are not able to maintain proper balance between the data utility and privacy and it may have the problem with privacy violations. This paper presents an Additive Rotation Perturbation approach for Privacy Preserving Data Mining (PPDM). In this proposed work, various dataset from UCI Machine Learning Repository was collected and it is protected with a New Additive Rotational Perturbation Technique under Privacy Preserving Data Mining. Experimental result shows that the proposed algorithm’s strength is high for all the datasets and it is estimated using the DoV (Difference of Variance) method.

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