Privacy-preserving data mining in homogeneous collaborative clustering
Joint Authors
Awdah, Muhammad
Salim, Samih A.
Ali, Ihab
Sad, al-Sayyid
Source
The International Arab Journal of Information Technology
Issue
Vol. 12, Issue 6 (31 Dec. 2015)10 p.
Publisher
Publication Date
2015-12-31
Country of Publication
Jordan
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Privacy concern has become an important issue in data mining.
In this paper, a novel algorithm for privacy preserving in distributed environment using data clustering algorithm has been proposed.
As demonstrated, the data is locally clustered and the encrypted aggregated information is transferred to the master site.
This aggregated information consists of centroids of clusters along with their sizes.
On the basis of this local information, global centroids are reconstructed then it is transferred to all sites for updating their local centroids.
Additionally, the proposed algorithm is integrated with Elliptic Curve Cryptography (ECC) public key cryptosystem and Diffie-Hellman Key Exchange.
The proposed distributed encrypted scheme can add an increase not more than 15% in performance time relative to distributed non encrypted scheme but give not less than 48% reduction in performance time relative to centralized scheme with the same size of dataset.
Theoretical and experimental analysis illustrates that the proposed algorithm can effectively solve privacy preserving problem of clustering mining over distributed data and achieve the privacy-preserving aim.
American Psychological Association (APA)
Awdah, Muhammad& Salim, Samih A.& Ali, Ihab& Sad, al-Sayyid. 2015. Privacy-preserving data mining in homogeneous collaborative clustering. The International Arab Journal of Information Technology،Vol. 12, no. 6.
https://search.emarefa.net/detail/BIM-431182
Modern Language Association (MLA)
Awdah, Muhammad…[et al.]. Privacy-preserving data mining in homogeneous collaborative clustering. The International Arab Journal of Information Technology Vol. 12, no. 6 (2015).
https://search.emarefa.net/detail/BIM-431182
American Medical Association (AMA)
Awdah, Muhammad& Salim, Samih A.& Ali, Ihab& Sad, al-Sayyid. Privacy-preserving data mining in homogeneous collaborative clustering. The International Arab Journal of Information Technology. 2015. Vol. 12, no. 6.
https://search.emarefa.net/detail/BIM-431182
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-431182