How to control the level of knowledge disclosure and secure certain confidential patterns is a subtask comparable to confidential data hiding in privacy preserving data mining. 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 original dataset and preserve its overall characteristics. A factor swapping method is designed to hide particular confidential 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 pattern and data value hiding. Under certain constraints on the nonnegative matrix factorization iterations, an optimal solution can be computed in which the user-specified confidential memberships or relationships are hidden without undesirable alterations on nonconfidential patterns. © 2007 IEEE.
CITATION STYLE
Wang, J., Zhang, J., Liu, L., & Han, D. (2007). Simultaneous pattern and data hiding in unsupervised learning. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 729–734). https://doi.org/10.1109/ICDMW.2007.83
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