Privacy Preservation for Distance and Correlation-based Mining Algorithms
Date Issued
2009
Date
2009
Author(s)
Su, Chun-Wei
Abstract
This paper devises a transformation scheme to protect data privacy in the case that data has to be sent to the third party for analysis purpose. Most conventional transformation schemes suffer from two limits, i.e. algorithm dependency and information loss. In this paper, we propose a novel privacy preserving scheme without these two limitations. This transformation algorithm is referred to as FISIP: FIrst and Second order sum and Inner product Preservation. Explicitly, as will be proved, by preserving three basic properties, (i.e. first order sum, second sum, and inner products) of private data, algorithms whose measures can be derived from the three properties can still be applied to public data transformed by FISIP. Specifically, distance and correlation can be derived from the three properties. Hence, distance-based algorithms and correlation-based algorithms can be applied. Evaluation of FISIP is done in two parts. The first part is data usefulness. The second part is data robustness. The two goals are intrinsically difficult to achieve at the same time. However, FISIP attains these two goals shown by our experimental results later. In all, FISIP is able to provide a transformation that preserves the distance and the correlation for the original private data after their transformation to the public data. As a result, while the privacy is protected, the mining quality from the transformed (public) data can be obtained to be the same as that from the original (private) data.
Subjects
data mining
privacy preserving
distance-based
correlation-based
Type
thesis
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