Abstract:
We propose a novel unified model for Privacy-Preserving
Support Vector Machines (PPSVM for short) classifier on horizontally and
vertically partitioned data. We prove the feasibility of the model. Besides
we give out the algorithms for horizontally partitioned data and vertically
partitioned data, respectively. The columns of data matrix A represent
input features and the rows represent the individual data which is called
a training/testing point in SVM. For horizontally partitioned data, the
data matrix A whose rows including all input features are divided into
groups belonging to different entities. While for vertically partitioned
data, the data matrix A`s columns are divided into groups belonging to
different entities. Each entity is unwilling to share its group of data
or leak the data for various reasons. The proposed SVM classifiers are
public but do not reveal any private data. And when we calculate the classifier
at last, we do not need to recover the original data. Besides, it has
comparable accuracy with that of an ordinary SVM classifier that uses
the centralized data set directly. Experiments show that our approach
is effective.
Fubo Shao, Hua Duan, Guoping He and Xin Zhang, 2008. A Unified Model for Privacy-Preserving Support Vector Machines on Horizontally and Vertically Partitioned Data. Information Technology Journal, 7: 850-858.
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