本文選題：數(shù)據(jù)發(fā)布　切入點：隱私保護　出處：《大連海事大學》2017年碩士論文　論文類型：學位論文

【摘要】：進入信息時代后,眾多的服務提供商積累了大量的用戶數(shù)據(jù)。數(shù)據(jù)共享可以避免由于雪藏數(shù)據(jù)帶來的浪費,但是共享的數(shù)據(jù)往往涉及用戶的隱私。因此,數(shù)據(jù)發(fā)布過程中的用戶隱私保護問題逐漸受到了學術(shù)界和工業(yè)界的關注。差分隱私保護模型以其優(yōu)秀的表現(xiàn),被應用到眾多領域。本文研究滿足差分隱私保護模型的數(shù)據(jù)發(fā)布算法,并針對數(shù)據(jù)類型的不同,提出不同的解決方案。本文的研究目的是在保證發(fā)布算法滿足差分隱私保護模型的基礎上,提高發(fā)布數(shù)據(jù)的準確性。針對數(shù)值型數(shù)據(jù),本文提出了基于有序劃分直方圖的數(shù)據(jù)發(fā)布算法。該算法對直方圖區(qū)間做第一重變換后,在保護直方圖結(jié)構(gòu)信息的前提下對區(qū)間順序進行調(diào)整,借此消除了可能出現(xiàn)的"分區(qū)截斷"問題。同時,本文使用基于貪婪思想的分區(qū)算法,綜合考慮分區(qū)后添加的Laplace噪聲對分區(qū)方案的影響。這避免了對分區(qū)個數(shù)的事先約定,提高了算法對不同數(shù)據(jù)集的適用性。本文提出的算法在保證發(fā)布的直方圖滿足差分隱私保護模型的基礎上,提高了作用于該直方圖的范圍查詢的準確性。針對用復雜圖表示的關聯(lián)數(shù)據(jù),本文提出了基于層次隨機圖分治的關聯(lián)數(shù)據(jù)發(fā)布算法。本文的出發(fā)點是用戶對這類數(shù)據(jù)的興趣點往往集中在社區(qū)內(nèi)。所以,結(jié)合社區(qū)發(fā)現(xiàn)的相關知識,本文提出對復雜圖中的邊進行不同程度的差分隱私保護的策略。本文提出的算法基于分治的思想,對社區(qū)進行層次隨機圖構(gòu)建,然后合并子層次隨機圖。這解決了復雜圖規(guī)模較大時,原始算法效率低下的問題。本文提出的算法對復雜圖進行分塊處理,并對數(shù)據(jù)發(fā)布的隱私性和數(shù)據(jù)可用性進行了權(quán)衡。在實驗環(huán)節(jié),按照節(jié)點度的分布和最短路徑分布等衡量標準,本文的算法更好地保持了原始復雜圖的性質(zhì)。
[Abstract]:After entering the information age, a large number of service providers have accumulated a large amount of user data. Data sharing can avoid waste caused by snow storage data, but the shared data often involves users' privacy. In the process of data release, the problem of user privacy protection has been paid more and more attention by academia and industry. It has been applied to many fields. In this paper, we study the data publishing algorithm which satisfies the differential privacy protection model, and aim at the different data types. Different solutions are proposed. The purpose of this paper is to improve the accuracy of published data on the basis of ensuring that the publishing algorithm meets the differential privacy protection model. In this paper, a data publishing algorithm based on ordered partitioning histograms is proposed. After the first transformation of histogram interval, the interval order is adjusted under the premise of preserving the histogram structure information. In this way, the problem of partition truncation may be eliminated. At the same time, this paper uses the partition algorithm based on greedy thought to consider the effect of the Laplace noise added after partition on the partition scheme, which avoids the prior agreement on the number of partitions. The algorithm proposed in this paper ensures that the published histogram satisfies the differential privacy protection model, and improves the applicability of the algorithm to different data sets. Improves the accuracy of the range query that acts on the histogram. This paper proposes an algorithm for publishing associated data based on hierarchical random graph partition and conquer. The starting point of this paper is that the user's interest in this kind of data is often concentrated in the community. In this paper, a differential privacy protection strategy for edge in complex graphs is proposed. Based on the idea of divide-and-conquer, the proposed algorithm is used to construct hierarchical random graph of community. Then the sub-hierarchical random graph is merged. This solves the problem of low efficiency of the original algorithm when the scale of complex graph is large. The algorithm proposed in this paper deals with the complex graph in blocks. In the experiment, according to the distribution of node degree and the distribution of the shortest path, the algorithm of this paper can better maintain the nature of the original complex graph.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP309

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本文編號：1600293