Deriving private information from arbitrarily projected data

Authors:
Songtao Guo;Xintao Wu
Affiliations:
University of North Carolina at Charlotte;University of North Carolina at Charlotte
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

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Abstract

Distance-preserving projection based perturbation has gained much attention in privacy-preserving data mining in recent years since it mitigates the privacy/accuracy tradeoff by achieving perfect data mining accuracy. One apriori knowledge PCA based attack was recently investigated to show the vulnerabilities of this distance-preserving projected based perturbation approach when a sample dataset is available to attackers. As a result, non-distance-preserving projection was suggested to be applied since it is resilient to the PCA attack with the sacrifice of data mining accuracy to some extent. In this paper we investigate how to recover the original data from arbitrarily projected data and propose AKICA, an Independent Component Analysis based reconstruction method. Theoretical analysis and experimental results show that both distance-preserving and non-distance-preserving projection approaches are vulnerable to this attack. Our results offer insight into the vulnerabilities of projection based approach and suggest a careful scrutiny when it is applied in privacy-preserving data mining.