【摘要】：針對(duì)現(xiàn)有數(shù)據(jù)發(fā)布隱私保護(hù)保護(hù)算法中的"局部最優(yōu)"劃分問題,提出了一種基于KD樹最優(yōu)投影劃分的k匿名算法.首先,在全局范圍內(nèi)對(duì)每一個(gè)屬性維度進(jìn)行遍歷,根據(jù)投影距離方差值衡量每個(gè)維度的離散度,并確定最優(yōu)維度;然后,在最優(yōu)屬性維度上,計(jì)算其劃分系數(shù)值,并確定最優(yōu)劃分點(diǎn).進(jìn)一步引入一種改進(jìn)的KD樹結(jié)構(gòu),與傳統(tǒng)的KD樹結(jié)點(diǎn)是一個(gè)數(shù)據(jù)點(diǎn)不同,新設(shè)計(jì)的KD樹中的每個(gè)結(jié)點(diǎn)均是一個(gè)集合.用經(jīng)過(guò)劃分點(diǎn)并垂直于最優(yōu)維度的超平面將一個(gè)結(jié)點(diǎn)分成兩部分,分別作為其左、右孩子結(jié)點(diǎn).最后通過(guò)理論分析證明了本文算法的正確性,用實(shí)驗(yàn)比較和驗(yàn)證了算法的性能,實(shí)驗(yàn)結(jié)果顯示所提算法平均概化范圍減小10%~22%,能夠?qū)崿F(xiàn)更優(yōu)的劃分和更好的數(shù)據(jù)集可用性.
[Abstract]:A k-anonymous algorithm based on optimal projection partition of KD tree is proposed to solve the problem of "local optimal" partitioning in existing privacy protection algorithms for data publishing. Firstly, every attribute dimension is traversed in the global scope, the dispersion of each dimension is measured according to the difference of projection distance, and the optimal dimension is determined. Then, in the optimal attribute dimension, the partition coefficient is calculated and the optimal partition point is determined. Furthermore, an improved KD tree structure is introduced, which is different from the traditional KD tree node is a data point, each node in the newly designed KD tree is a set. A node is divided into two parts by a hyperplane perpendicular to the optimal dimension and divided into two parts as the left and right child nodes respectively. Finally, the correctness of the algorithm is proved by theoretical analysis. The performance of the algorithm is compared and verified by experiments. The experimental results show that the average generalizability range of the proposed algorithm is reduced by 10% 22%. It can achieve better partition and better data set availability.
【作者單位】： 安徽師范大學(xué)數(shù)學(xué)計(jì)算機(jī)科學(xué)學(xué)院;安徽師范大學(xué)網(wǎng)絡(luò)與信息安全工程研究中心;
【基金】：國(guó)家自然科學(xué)基金(61672039,61370050) 安徽省自然科學(xué)基金(1508085QF133)
【分類號(hào)】：TP309

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