Secure Data Analysis in Clusters (Iris Database)

2017 ◽  
pp. 52-61
Author(s):  
Raghvendra Kumar ◽  
Prasant Kumar Pattnaik ◽  
Priyanka Pandey
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Privacy Preservation ◽  
Data Communication ◽  
Distributed Database ◽  
Privacy Preserving ◽  
Data Mining Algorithm ◽  
Data Set ◽  
Mining Algorithm ◽  
Secure Data

This chapter used privacy preservation techniques (Data Modification) to ensure Privacy. Privacy preservation is another important issue. A picture, where number of clients owning their clustered databases (Iris Database) wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information and requires the privacy of the privileged information. There are numbers of efficient protocols are required for privacy preserving in data mining. This chapter presented various privacy preserving protocols that are used for security in clustered databases. The Xln(X) protocol and the secure sum protocol are used in mutual computing, which can defend privacy efficiently. Its focuses on the data modification techniques, where it has been modified our distributed database and after that sanded that modified data set to the client admin for secure data communication with zero percentage of data leakage and also reduce the communication and computation complexity.

scholarly journals A NOVEL PRIVACY PRESERVING DATA MINING ALGORITHM

2011 ◽  
Vol 11 (ASAT CONFERENCE) ◽  
pp. 1-17
Author(s):  
Fahmy Aly ◽  
Fakhry Medhat ◽  
M. Hanafy ◽  
EI-Zeweidy Aly
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Data Mining Algorithm ◽  
Privacy Preserving Data Mining ◽  
Mining Algorithm

scholarly journals A Review on Security Techniques for Privacy-Preserving in Data Mining

2019 ◽  
pp. 118-122
Author(s):  
K. S Chandwani ◽  
Anjali Mishra ◽  
Ayushi Desai ◽  
Priyanka Kushwaha ◽  
Sonal Nikose ◽  
...  
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Privacy Preserving ◽  
Data Mining Algorithm ◽  
Sensitive Data ◽  
Individual Level ◽  
Mining Algorithm ◽  
Class Level

As the rise in the data mining algorithm the extraction of knowledge from the large data is getting easy. But due to this new problem of Privacy of the knowledge from the stored data at various servers is introduced. So, it is required to provide privacy of the sensitive data from the data miners. This paper focuses on various approaches implemented by the miners for preserving of information at individual level, class level, etc. A detail description with limitation of different techniques security of privacy preserving is explained.

A Distributed Association Rules Mining Algorithm

2014 ◽  
Vol 971-973 ◽  
pp. 1459-1462
Author(s):  
Wen Liang Cao ◽  
Li Ping Chen
Keyword(s):  
Data Mining ◽  
Production Control ◽  
Distributed Database ◽  
Frequent Itemsets ◽  
Data Mining Algorithm ◽  
Distributed Data ◽  
Information Industry ◽  
Data Mining Algorithms ◽  
Mining Algorithm ◽  
Communication Traffic

Data mining has attracted a great deal of attention in the information industry in recent years and can be used for applications rangning from business management, production control, and science exploration etc. Most of the existing data mining algorithms are processing in the centralized systems; however, at present large database is usually distributed. Compared with the frequent itemsets lost and high communication traffic in distributed database conventional and improved algorithm FDM, An improved distributed data mining algorithm LTDM based on association roles is proposed. LTDM algorithm introduces the mapping indicated array mechanism to keep the integrity of frequent itemsets and decrease the communication traffic. The experimental results prove the efficiency of the proposed algorithm. The algorithm can be applied to information retrieval and so on in the digital library.

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