DocumentCode :
3190854
Title :
Simultaneous Pattern and Data Hiding in Unsupervised Learning
Author :
Wang, Jie ; Zhang, Jun ; Liu, Lian ; Han, Dianwei
fYear :
2007
fDate :
28-31 Oct. 2007
Firstpage :
729
Lastpage :
734
Abstract :
How to control the level of knowledge disclosure and se- cure certain confidential patterns is a subtask comparable to confidential data hiding in privacy preserving data min- ing. We propose a technique to simultaneously hide data values and confidential patterns without undesirable side effects on distorting nonconfidential patterns. We use non- negative matrix factorization technique to distort the origi- nal dataset and preserve its overall characteristics. A fac- tor swapping method is designed to hide particular confi- dential patterns for k-means clustering. The effectiveness of this novel hiding technique is examined on a benchmark dataset. Experimental results indicate that our technique can produce a single modified dataset to achieve both pat- tern and data value hiding. Under certain constraints on the nonnegative matrix factorization iterations, an optimal solution can be computed in which the user-specified con- fidential memberships or relationships are hidden without undesirable alterations on nonconfidential patterns.
Keywords :
Collaboration; Computer science; Conferences; Data encapsulation; Data mining; Data privacy; Design methodology; Matrix decomposition; Protection; Unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on
Conference_Location :
Omaha, NE
Print_ISBN :
978-0-7695-3019-2
Electronic_ISBN :
978-0-7695-3033-8
Type :
conf
DOI :
10.1109/ICDMW.2007.83
Filename :
4476749
Link To Document :
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