Anonymizing 1:M microdata with high utility.
Anonymizing 1:M microdata with high utility.
复制标题
1:M 微数据匿名化，实用性高
DOI：
10.1016/j.knosys.2016.10.012
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发表时间：
2017-01-01
影响因子：
8.8
通讯作者：
Li XB
中科院分区：
文献类型：
--
作者：
Gong Q;Luo J;Yang M;Ni W;Li XB
在数据发布和数据挖掘过程中，保护隐私和效用对于个人、数据提供者和研究人员来说至关重要。然而，该领域的研究通常假设一个人在数据集中只有一条记录，这在许多应用中是不现实的。拥有个人的多个记录会导致新的隐私泄露。我们称这样的数据集为1:M数据集。在本文中，我们提出了一个新的隐私模型，称为(k, l)-多样性，以解决1:M数据发布中的披露风险。在此模型的基础上，我们开发了一种高效的1:m -概化算法来保护隐私和数据效用，并与其他方法进行了比较。对真实世界数据的大量实验表明，我们的方法在数据效用和计算成本方面优于最先进的技术。
Preserving privacy and utility during data publishing and data mining is essential for individuals, data providers and researchers. However, studies in this area typically assume that one individual has only one record in a dataset, which is unrealistic in many applications. Having multiple records for an individual leads to new privacy leakages. We call such a dataset a 1:M dataset. In this paper, we propose a novel privacy model called (k, l)-diversity that addresses disclosure risks in 1:M data publishing. Based on this model, we develop an efficient algorithm named 1:M-Generalization to preserve privacy and data utility, and compare it with alternative approaches. Extensive experiments on real-world data show that our approach outperforms the state-of-the-art technique, in terms of data utility and computational cost.