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- 저자 : Zhonglian Hu (검색결과 2 건)
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Differential Privacy via Weighted Sampling Set Cover
Zhonglian Hu,Zhaobin Liu,Yangyang Xu,Zhiyang Li 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.4
Differential privacy is a security guarantee model which widely used in privacy preserving data publishing, but the query result can’t be used in data research directly, especially in high-dimensional datasets. To address this problem, we propose a dimensionality reduction method. The core idea of this method is using a series of low-dimensional datasets to reconstruct a high-dimensional dataset, it improves data availability eventually. The main issue of this method is the reconstruction integrity, so a special sampling via set cover model is proposed in this article, which builds a multidimensional composite marginal tables set as a new middleware in differential privacy model. As a result, any form of disjunctive queries can be answered, and the accuracy of data query is improved. The experiment results also show the effectiveness of our method in practice.
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Candidate Pruning-Based Differentially Private Frequent Itemsets Mining
Yangyang Xu,Zhaobin Liu,Zhonglian Hu,Zhiyang Li 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.7
Frequent Itemsets Mining(FIM) is a typical data mining task and has gained much attention. Due to the consideration of individual privacy, various studies have been focusing on privacy-preserving FIM problems. Differential privacy has emerged as a promising scheme for protecting individual privacy in data mining against adversaries with arbitrary background knowledge. In this paper, we present an approach to exploring frequent itemsets under rigorous differential privacy model, a recently introduced definition which provides rigorous privacy guarantees in the presence of arbitrary external information. The main idea of differentially privacy FIM is perturbing the support of item which can hide changes caused by absence of any single item. The key observation is that pruning the number of unpromising candidate items can effectively reduce noise added in differential privacy mechanism, which can bring about a better tradeoff between utility and privacy of the result. In order to effectively remove the unpromising items from each candidate set, we use a progressive sampling method to get a super set of frequent items, which is usually much smaller than the original item database. Then the sampled set will be used to shrink candidate set. Extensive experiments on real data sets illustrate that our algorithm can greatly reduce the noise scale injected and output frequent itemsets with high accuracy while satisfying differential privacy.
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