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.
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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