DiPCoDing: A Differentially Private Approach for Correlated Data with Clustering

André L. C. Mendonça, Felipe T. Brito, L. S. Linhares, Javam C. Machado
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引用次数: 4

Abstract

Differential privacy is a model which gives strong privacy guarantees. It was designed to make difficult to distinguish individuals' records on statistical databases while maximizing data utility. Differential privacy approaches usually assume that database records are sampled independently, i.e., each record of this database is independent of the rest. However, this assumption is not always true in the context of real-world applications. In this paper we propose DiPCoDing, a novel approach to calculate the correlation between records in statistical databases using clusterization. For this matter, we have considered Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM). Our method aims to group similar records, which are more likely to be correlated, to reduce the sensitivity of differential privacy and consequently the amount of noise added to the query answer, increasing data utility while providing privacy for correlated data. The experimental results of our approach showed that relative errors and noisy answers are significantly lower than those from existing works.
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DiPCoDing:具有聚类的相关数据的差分私有方法
差分隐私是一种提供强隐私保障的模型。它的目的是使统计数据库中的个人记录难以区分,同时使数据效用最大化。差分隐私方法通常假设数据库记录是独立采样的,即该数据库的每条记录都独立于其他记录。然而,在实际应用程序的上下文中,这个假设并不总是正确的。在本文中,我们提出了DiPCoDing,一种利用聚类计算统计数据库中记录之间相关性的新方法。为此,我们考虑了基于密度的噪声应用空间聚类(DBSCAN)和高斯混合模型(GMM)。我们的方法旨在对更有可能相关的相似记录进行分组,以降低差异隐私的敏感性,从而降低添加到查询答案中的噪声量,在为相关数据提供隐私的同时提高数据效用。实验结果表明,该方法的相对误差和噪声答案明显低于现有方法。
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