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A Privacy Preservation Method for High Dimensional Data Mining
Author(s): YANG Jing, ZHAO Jia-shi, ZHANG Jian-pei
Pages: 2187-
2192
Year: 2013
Issue:
11
Journal: Acta Electronica Sinica
Keyword: privacy preservation; high dimensional data mining; hash technique; random projection; secure subspace;
Abstract: This paper proposes a privacy preservation method based on random projection to overcome the curse of dimen-sionality in privacy preserving data mining .To prevent leaks of random matrix which can lead to the reconstruction attack ,it first proposes the concepts of secure subspace and secure subspace mapping .Then ,it constructs a secure subspace mapping using hash technique ,which is implemented by a random projection matrix ,and it achieves a low distortion embedding while preserving the data privacy .Finally ,it proves that the secure subspace can preserve the Euclidean distance and inner product between any two original points .The experimental results show that the proposed technique can ensure the data quality in different data mining applications ef-fectively under the precondition of preserving data privacy .
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