本文關(guān)鍵詞：面向加權(quán)社會(huì)網(wǎng)絡(luò)最短路徑的隱私保護(hù)方法研究　出處：《廣西大學(xué)》2016年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 加權(quán)社會(huì)網(wǎng)絡(luò) 隱私保護(hù) 邊權(quán)重?cái)_動(dòng) 最短路徑

【摘要】：社會(huì)網(wǎng)絡(luò)上的數(shù)據(jù)規(guī)模伴隨著互聯(lián)網(wǎng)技術(shù)發(fā)展的步伐不斷增大,如此巨量的數(shù)據(jù)給研究人員進(jìn)行知識(shí)挖掘以及數(shù)據(jù)分析工作提供便利的同時(shí),隱藏在社會(huì)網(wǎng)絡(luò)數(shù)據(jù)下的個(gè)人隱私信息的保護(hù)問(wèn)題也隨之而來(lái)。然而在加權(quán)社會(huì)網(wǎng)絡(luò)發(fā)布之前,數(shù)據(jù)若未經(jīng)處理則可能造成用戶隱私泄露。需要注意的是,最短路徑通常包含大量隱私信息,因此針對(duì)加權(quán)社會(huì)網(wǎng)絡(luò)上的最短路徑的保護(hù)顯得尤為重要�；诖�,本文的研究?jī)?nèi)容主要針對(duì)如何保護(hù)加權(quán)社會(huì)網(wǎng)絡(luò)中最短路徑上的權(quán)重信息和路徑信息,本文主要工作如下：1)針對(duì)敏感節(jié)點(diǎn)對(duì)最短路徑上的邊權(quán)重泄露問(wèn)題,提出了基于高斯分布的貪心擾動(dòng)算法(Greedy Perturbation algorithm based on Gaussian Distribution, GPGD)。該算法不僅能夠防止敏感節(jié)點(diǎn)對(duì)最短路徑上邊權(quán)重泄露,而且利用高斯分布的特性,有效改善了傳統(tǒng)貪心擾動(dòng)算法中敏感節(jié)點(diǎn)對(duì)最短路徑長(zhǎng)度在擾動(dòng)前后存在較大差距問(wèn)題；2)針對(duì)敏感節(jié)點(diǎn)對(duì)最短路徑泄露問(wèn)題,提出了top-k路徑匿名隱私保護(hù)算法(Top-K-Single Path Anonymization Algorithm, TKSP)。該算法不僅能夠防止敏感節(jié)點(diǎn)對(duì)最短路徑泄露,還能保證擾動(dòng)前后圖數(shù)據(jù)特性一致,并且與KSP算法相比具有運(yùn)行時(shí)間短、圖數(shù)據(jù)保持性高及查詢準(zhǔn)確率高的優(yōu)點(diǎn)。
[Abstract]:The scale of data on the social network is increasing with the development of Internet technology. Such a large amount of data provides convenience for researchers to do knowledge mining and data analysis at the same time. The protection of personal privacy information hidden under the social network data also follows. However, before the weighted social network is released, the unprocessed data may cause user privacy disclosure. The shortest path usually contains a lot of privacy information, so it is very important to protect the shortest path on the weighted social network. This paper mainly focuses on how to protect the weight information and path information of the shortest path in the weighted social network. The main work of this paper is as follows: 1) aiming at the leakage of the edge weight of the sensitive node to the shortest path. A greedy perturbation algorithm based on Gao Si distribution is proposed. Greedy Perturbation algorithm based on Gaussian Distribution. The algorithm can not only prevent sensitive nodes from leakage of edge weights on the shortest path, but also make use of the characteristics of Gao Si distribution. It effectively improves the gap between the length of the shortest path and the sensitive node in the traditional greedy perturbation algorithm before and after disturbance. 2) aiming at the shortest path leakage problem of sensitive nodes. An anonymous privacy protection algorithm for top-k path named Top-K-Single Path Anonymization Algorithm is proposed. The algorithm not only can prevent sensitive nodes from leaking the shortest path, but also can ensure the same characteristics of graph data before and after disturbance, and has shorter running time than KSP algorithm. The advantages of high graph data retention and high query accuracy.
【學(xué)位授予單位】：廣西大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP393.08

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