Learning and Synchronized Privacy Preserving Frequent Pattern Mining

2011 ◽  
Vol 22 (8) ◽  
pp. 1749-1760
Author(s):  
Yu-Hong GUO ◽  
Yun-Hai TONG ◽  
Shi-Wei TANG ◽  
Leng-Dong WU

Design an encryption of privacy preserving and scheduling of intermediate datasets in cloud. Implemenation of encryption is done as follows: Identification of intermediate datasets that needs to be encrypted. Based on frequent pattern mining the least frequent intermediate datasets are encrypted. Perform column level encryption to the sensitive information. Predicting the data based on inference analysis would not be possible. So that the data will be secure when compared to the existing system.


Author(s):  
Carson K. Leung ◽  
Calvin S. H. Hoi ◽  
Adam G. M. Pazdor ◽  
Bryan H. Wodi ◽  
Alfredo Cuzzocrea

Information sharing among the associations is a general development in a couple of zones like business headway and exhibiting. As bit of the touchy principles that ought to be kept private may be uncovered and such disclosure of delicate examples may impacts the advantages of the association that have the data. Subsequently the standards which are delicate must be secured before sharing the data. In this paper to give secure information sharing delicate guidelines are bothered first which was found by incessant example tree. Here touchy arrangement of principles are bothered by substitution. This kind of substitution diminishes the hazard and increment the utility of the dataset when contrasted with different techniques. Examination is done on certifiable dataset. Results shows that proposed work is better as appear differently in relation to various past strategies on the introduce of evaluation parameters.


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