基于差分隐私的非等距直方图发布方法

doi:10.11959/j.issn.2096-109x.2020035

Abstract

Abstract:

Existing histogram publishing technology based on differential privacy may show phenomenon of＆quot;retracting＆quot; and ＆quot;zero bucket＆quot; when histogram is used to reflect the real distribution characteristics of data,and ＆quot;too gentle＆quot; in the case of large data volume.In addition,the existing technology of the original histogram difference of privacy protection when not considering the amount of information of each group is different.In view of the above problems,a kind of non-equal-width histogram publishing method based on differential privacy was proposed.First of all,a non-isometric histogram based on the sparseness of the data should bereasonably constructed by empirical distribution function.Secondly,differential privacy protection technology should be applied to non-equal-width histogram to protect the privacy of the original non-equal-width histogram.Finally,the privacy budget should be set for each group according to the class widths of the non-equal-width histogram to improve the privacy of each group of data.The experimental results show that the sparseness of the data distribution is fully taken into account when using the proposed method to perform histogram publishing under differential privacy,effectively avoid the phenomenon of histogram with “retracting” and “zero barrels”,and the accuracy of the published histogram for reflecting the characteristics of the data distribution is guaranteed.Also,when adding noise in line with Laplace mechanism to each group,setting a reasonable privacy budget for each group according to the class widths to some extent increases the privacy of different data segments.

Key words: differential privacy, non-equal-width, histogram publishing, Laplace mechanism, privacy budget

CLC Number:

TP309.2

Lei YANG,Xiao ZHENG,Wei ZHAO. Non-equal-width histogram publishing method based on differential privacy[J]. Chinese Journal of Network and Information Security, 2020, 6(3): 39-49.

Figures/Tables 11

算法	Error(C₁)
DiffHR	$\sum_{H_{j} \in C_{i}} {(H_{j} - {\bar{C}}_{i})}^{2} + \frac{2}{\| C_{i} \| ε_{2}^{2}}$
APB	$\sum_{H_{j} \in C_{i}} {(H_{j} - {\bar{C}}_{i})}^{2} + \frac{2}{\| C_{i} \| ε_{2}^{2}}$
NHPDP	$\sum_{H_{j} \in C_{i}} {(H_{j} - {\bar{C}}_{i})}^{2} + \frac{2}{\| C_{i} \| ε_{}^{2}}$

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Non-equal-width histogram publishing method based on differential privacy

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