模式识别与人工智能
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模式识别与人工智能  2015, Vol. 28 Issue (12): 1084-1092    DOI: 10.16451/j.cnki.issn1003-6059.201512004
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面向任意区间树结构的差分隐私直方图发布算法*
吴英杰,陈鸿,王一蕾,孙岚
福州大学 数学与计算机科学学院 福州 350116
A Differentially Private Histogram Publication Algorithm for Arbitrary Range Tree Structure
WU Ying-Jie, CHEN Hong, WANG Yi-Lei, SUN Lan
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116

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摘要 当前一种有效的差分隐私直方图发布是先将直方图映射成满m叉区间树,后利用查询一致性约束提高查询精度.然而,并非所有直方图都能映射成满m叉区间树.针对此问题,文中首先提出可实现任意直方图向树结构映射的k-区间树;然后从理论上分析对于任意区间树结构下的差分隐私直方图发布,仍可在一致性约束下利用最优线性无偏估计进一步降低区间计数查询的误差;最后提出面向任意区间树结构基于局部最优线性无偏估计的差分隐私直方图发布算法(LBLUE).实验对比分析同类算法和LBLUE所发布数据的区间计数查询精度及算法效率,表明LBLUE有效可行.
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吴英杰
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孙岚
关键词 差分隐私直方图发布任意区间树结构最优线性无偏估计    
Abstract:In the existing methods for differentially private histogram publication, a histogram is mapped to a perfect m-ary range tree. The accuracies of queries are boosted through consistency constraints of the queries. However, not all histograms in real application can be mapped to perfect m-ary range trees directly. In this paper, a range tree structure, k-range tree, is firstly put forward. By k-range tree, an arbitrary histogram is mapped to a range tree. Secondly, the theoretical analysis shows that for differentially private histogram publication for arbitrary range tree structure, the error of range counting queries still can be further reduced by solving the best linear unbiased estimation of the tree node values through consistency. Finally, a differentially private histogram publication algorithm based on local best linear unbiased estimation(LBLUE) for arbitrary range tree structure is proposed. Experiment is carried out to compare LBLUE and the traditional algorithms on the accuracy of range counting queries in the released histogram and the algorithm efficiency. Experimental results show that LBLUE is effective and feasible.
Key wordsDifferential Privacy    Histogram Publication    Arbitrary Rang Tree Structure    Best Linear Unbiased Estimation   
收稿日期: 2014-12-09     
ZTFLH: TP 309  
基金资助:国家自然科学基金项目(No.61300026)、福建省自然科学基金项目(No.2014J01230)资助
作者简介: 吴英杰,男,1979年生,博士,副教授,主要研究方向为数据挖掘、数据安全隐私保护.E-mail:yjwu@fzu.edu.cn.陈鸿,男,1989年生,硕士研究生,主要研究方向为数据隐私保护.王一蕾(通讯作者),女,1979年生,博士研究生,主要研究方向为推荐系统、数据隐私保护.E-mail:yilei@fzu.edu.cn.孙岚,女,1978年生,硕士,讲师,主要研究方向为数据隐私保护.
引用本文:   
吴英杰,陈鸿,王一蕾,孙岚. 面向任意区间树结构的差分隐私直方图发布算法*[J]. 模式识别与人工智能, 2015, 28(12): 1084-1092. WU Ying-Jie, CHEN Hong, WANG Yi-Lei, SUN Lan. A Differentially Private Histogram Publication Algorithm for Arbitrary Range Tree Structure. , 2015, 28(12): 1084-1092.
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