An Accurate Privacy-Preserving Data Mining Algorithm for Frequent Itemsets in Distributed Databases

2008 ◽  
Author(s):  
Xiaodan Hu ◽  
Yongchu Wang
Keyword(s):  
Data Mining ◽  
Distributed Databases ◽  
Privacy Preserving ◽  
Frequent Itemsets ◽  
Data Mining Algorithm ◽  
Privacy Preserving Data Mining ◽  
Mining Algorithm

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

Research on Association Rule Mining Algorithm Based on Distributed Data

2014 ◽  
Vol 998-999 ◽  
pp. 899-902 ◽  
Author(s):  
Cheng Luo ◽  
Ying Chen
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Scale ◽  
Frequent Itemsets ◽  
Network Communication ◽  
Data Mining Algorithm ◽  
Distributed Data ◽  
Rule Mining ◽  
Mining Algorithm

Existing data miming algorithms have mostly implemented data mining under centralized environment, but the large-scale database exists in the distributed form. According to the existing problem of the distributed data mining algorithm FDM and its improved algorithms, which exist the problem that the frequent itemsets are lost and network communication cost too much. This paper proposes a association rule mining algorithm based on distributed data (ARADD). The mapping marks the array mechanism is included in the ARADD algorithm, which can not only keep the integrity of the frequent itemsets, but also reduces the cost of network communication. The efficiency of algorithm is proved in the experiment.

Secure Computation for Privacy Preserving Data Mining

2011 ◽  
pp. 1747-1752 ◽  
Author(s):  
Yehuda Lindell
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Secure Computation ◽  
Data Mining Algorithm ◽  
Privacy Preserving Data Mining ◽  
Public And Private ◽  
The Public ◽  
Use Of Data ◽  
To Come ◽  
Mining Tools

The increasing use of data mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy preserving data mining solutions achieve the somewhat paradoxical property of enabling a data mining algorithm to use data without ever actually “seeing” it. Thus, the benefits of data mining can be enjoyed, without compromising the privacy of concerned individuals.

scholarly journals Secure Multiparty Computation for Privacy Preserving Data Mining

2011 ◽  
pp. 1005-1009 ◽  
Author(s):  
Yehida Lindell
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Data Mining Algorithm ◽  
Multiparty Computation ◽  
Privacy Preserving Data Mining ◽  
Public And Private ◽  
The Public ◽  
Use Of Data ◽  
To Come ◽  
Mining Tools

The increasing use of data-mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy-preserving data-mining solutions achieve the somewhat paradoxical property of enabling a data-mining algorithm to use data without ever actually seeing it. Thus, the benefits of data mining can be enjoyed without compromising the privacy of concerned individuals.

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