Title:
A Random Rotation Perturbation Approach to Privacy Preserving Data Classification
A Random Rotation Perturbation Approach to Privacy Preserving Data Classification
Author(s)
Chen, Keke
Liu, Ling
Liu, Ling
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Abstract
This paper presents a random rotation perturbation
approach for privacy preserving data classification.
Concretely, we identify the importance
of classification-specific information with respect
to the loss of information factor, and present a
random rotation perturbation framework for privacy
preserving data classification. Our approach
has two unique characteristics. First, we identify
that many classification models utilize the geometric
properties of datasets, which can be preserved
by geometric rotation. We prove that the
three types of classifiers will deliver the same performance
over the rotation perturbed dataset as
over the original dataset. Second, we propose a
multi-column privacy model to address the problems
of evaluating privacy quality for multidimensional
perturbation. With this metric, we develop
a local optimal algorithm to find the good
rotation perturbation in terms of privacy guarantee.
We also analyze both naive estimation and
ICA-based reconstruction attacks with the privacy
model. Our initial experiments show that the random
rotation approach can provide high privacy
guarantee while maintaining zero-loss of accuracy
for the discussed classifiers.
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Date Issued
2005
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182973 bytes
Resource Type
Text
Resource Subtype
Technical Report