Title :
A privacy preserving clustering technique using hybrid data transformation method
Author :
Li, Liming ; Zhang, Qishan
Author_Institution :
Sch. of Manage., Fuzhou Univ., Fuzhou, China
Abstract :
Despite many successful stories of data mining in a wide range of applications, this technique has raised some issues related to privacy and security of individuals. Due to these issues, data owners are often unwilling to share their sensitive information with data miners. In this paper, we present a novel method for privacy preserving clustering over centralized data. The proposed method is built upon the application of double-reflecting data perturbation method (DRDP) and rotation based translation (RBT) in order to provide secrecy of confidential numerical attributes without losing accuracy in results. The experiments demonstrate that the proposed method is effective and provides a feasible approach to balancing privacy and accuracy.
Keywords :
data mining; data privacy; pattern clustering; confidential numerical attribute secrecy; data mining; double-reflecting data perturbation method; hybrid data transformation method; privacy balancing approach; privacy preserving clustering technique; rotation based translation; Algorithm design and analysis; Clustering algorithms; Data analysis; Data mining; Data privacy; Hybrid intelligent systems; Machine learning algorithms; Perturbation methods; Protection; Sliding mode control;
Conference_Titel :
Grey Systems and Intelligent Services, 2009. GSIS 2009. IEEE International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4914-9
Electronic_ISBN :
978-1-4244-4916-3
DOI :
10.1109/GSIS.2009.5408151
