Applying Privacy Preserving Count Aggregate Queries to k-Classification

Authors:
Hidehisa Takamizawa;Masayoshi Aritsugi
Affiliations:
Department of Computer Science, Graduate School of Engineering, Gunma University, 1-5-1 Tenjin-cho, Kiryu, Gunma, 376-8515, Japan;Computer Science and Electrical Engineering, Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japan
Venue:
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Year:
2007

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Abstract

It is important to process data effectively while preserving privacy of personal information. In this paper, we propose a technique to reconstruct results of count aggregate queries from a perturbed table for building a decision tree whose target attribute contains more than two classes. Using the conventional technique, we must reconstruct the results of target values from those of each value calculated independently in such the case. In this paper, we borrow and extend the conventional technique to reconstruct the results of target values at once. We also report some experimental results showing that our proposal can reduce reconstruction errors compared to the conventional technique in cases where perturbation ratio is high.