CALM: Consistent Adaptive Local Marginal for Marginal Release under Local Differential Privacy
CALM: Consistent Adaptive Local Marginal for Marginal Release under Local Differential Privacy
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DOI：
10.1145/3243734.3243742
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发表时间：
2018-10
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影响因子：
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通讯作者：
Zhikun Zhang;Tianhao Wang;Ninghui Li;Shibo He;Jiming Chen
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文献类型：
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作者：
Zhikun Zhang;Tianhao Wang;Ninghui Li;Shibo He;Jiming Chen
边缘表是捕获一组属性之间的相关性的主力。我们考虑在给定一组用户的多维数据的情况下构建边缘表的问题，同时满足本地差分隐私（LDP），这是一种不依赖可信第三方的隐私概念，可以保护个人用户的隐私。针对该问题的现有研究在高维环境中表现不佳；更糟糕的是，有些会产生非常昂贵的计算开销。在本文中，我们提出了 CALM（一致自适应局部边际），它利用了仔细的挑战分析，并且始终比现有方法表现得更好。更重要的是，CALM 可以很好地适应大数据维度和边际大小。我们对几个现实世界的数据集进行了广泛的实验。实验结果证明了 CALM 相对于现有方法的有效性和效率。
Marginal tables are the workhorse of capturing the correlations among a set of attributes. We consider the problem of constructing marginal tables given a set of user's multi-dimensional data while satisfying Local Differential Privacy (LDP), a privacy notion that protects individual user's privacy without relying on a trusted third party. Existing works on this problem perform poorly in the high-dimensional setting; even worse, some incur very expensive computational overhead. In this paper, we propose CALM, Consistent Adaptive Local Marginal, that takes advantage of the careful challenge analysis and performs consistently better than existing methods. More importantly, CALM can scale well with large data dimensions and marginal sizes. We conduct extensive experiments on several real world datasets. Experimental results demonstrate the effectiveness and efficiency of CALM over existing methods.