抗CFP攻擊的社交網(wǎng)絡(luò)隱私保護算法研究
發(fā)布時間：2018-10-20 17:05
【摘要】：隨著互聯(lián)網(wǎng)和大數(shù)據(jù)時代的到來,互聯(lián)網(wǎng)給人們帶來了巨大生活便利,但也使得人們的隱私保護受到很大程度上的威脅。因為相對于數(shù)據(jù)信息傳播速度不那么發(fā)達的時期,在現(xiàn)有網(wǎng)絡(luò)環(huán)境下收集、整合、分析和傳播用戶信息要容易的多,所以會導(dǎo)致用戶信息更易于泄露。因此在互聯(lián)網(wǎng)上如何保護個人隱私成為研究的熱點問題。目前,已有很多關(guān)于社交網(wǎng)絡(luò)隱私保護的方法和模型,其中最經(jīng)典的是k-匿名社交網(wǎng)絡(luò)隱私保護算法。它要求在k-匿名數(shù)據(jù)集里,每條具有標(biāo)識的記錄至少有k-1個記錄與之相同。因此,k-匿名社交網(wǎng)絡(luò)隱私保護算法在一定程度上保護了個人隱私。但是,現(xiàn)有的k-匿名技術(shù)在進行隱私保護時,將社交網(wǎng)絡(luò)中的節(jié)點全部設(shè)為私有,忽略了實際網(wǎng)絡(luò)中存在大量公有節(jié)點。這些公有節(jié)點身份是公開的,攻擊者可以利用它們與私有節(jié)點之間的連接作為背景知識對私有節(jié)點進行再識別攻擊,即Connection Fingerprint(CFP)攻擊。原有的抗CFP抗擊隱私保護算法很好地保護了公有節(jié)點的中心性,但是仍有不足之處,沒有盡可能多地考慮社交網(wǎng)絡(luò)圖性質(zhì)。本文在此基礎(chǔ)上提出了一種改進的抗CFP社交網(wǎng)絡(luò)隱私保護算法。主要工作有:第一,分析原有的抗CFP攻擊的社交網(wǎng)絡(luò)隱私保護算法。針對CFP攻擊,現(xiàn)有的社交網(wǎng)絡(luò)隱私保護算法在實施邊替換時隨機選取等價組中的私有節(jié)點,忽略了網(wǎng)絡(luò)圖中各私有節(jié)點的中心性等。第二,針對原有的抗CFP攻擊隱私保護算法,即K-anony算法考慮圖性質(zhì)的不足,提出一種改進的抗CFP攻擊隱私保護算法——N-hop-K-anony算法。其思想是:在n跳范圍內(nèi),對任意私有節(jié)點v都至少有其余k-1個節(jié)點與其所連接的公共節(jié)點相同。N-hop-K-anony在進行節(jié)點邊替換時,從社交網(wǎng)絡(luò)圖性質(zhì)的幾個評價標(biāo)準(zhǔn)出發(fā),最終選取網(wǎng)絡(luò)聚集系數(shù)作為其理論依據(jù),對原有算法進行改進。改進后的算法在邊替換上做出處理,并編碼實現(xiàn)改進前后的算法。第三,在email-Eu-core、College Msg、Facebook和ca-Gr Qc四個真實有效的數(shù)據(jù)集上進行改進前后算法的對比實驗。通過對比實驗可以發(fā)現(xiàn):在時間性能基本一致的情況下,算法改進后在一定程度上比改進前更能夠保護節(jié)點中心性,尤其是緊密中心性和介數(shù)中心性;在網(wǎng)絡(luò)聚集系數(shù)上,算法改進后也比改進前具有較好的實驗效果。
[Abstract]:With the advent of the Internet and big data era, the Internet has brought great convenience to people, but also make people's privacy protection is threatened to a great extent. It is much easier to collect, integrate, analyze and disseminate user information in the existing network environment than in the period when the speed of data dissemination is not so developed. Therefore, how to protect personal privacy on the Internet has become a hot issue. At present, there are many methods and models of social network privacy protection, among which the most classical is the k-anonymous social network privacy protection algorithm. It requires at least K-1 records to be identical to each identified record in a k- anonymous dataset. Therefore, k-anonymous social network privacy protection algorithm to a certain extent to protect personal privacy. However, the existing k- anonymity technology in privacy protection, all the nodes in the social network are set private, ignoring the existence of a large number of public nodes in the actual network. The identity of these public nodes is public and the attacker can use the connection between them and the private node as the background knowledge to re-identify the private node attack, that is, the Connection Fingerprint (CFP) attack. The original anti-CFP privacy protection algorithm protects the centrality of public nodes well, but there are still some shortcomings, and the nature of social network graph is not considered as much as possible. In this paper, an improved privacy protection algorithm against CFP social networks is proposed. The main work is as follows: first, the original privacy protection algorithm against CFP attack is analyzed. For CFP attacks, the existing privacy protection algorithms of social networks randomly select the private nodes in the equivalent group when implementing edge substitution, ignoring the centrality of each private node in the network diagram. Secondly, an improved privacy protection algorithm (N-hop-K-anony) against CFP attacks is proposed, which is an improved privacy protection algorithm against CFP attacks, that is, K-anony algorithm takes into account the shortcomings of graph properties. The idea is: in the n-hop range, at least the remaining k-1 nodes for any private node v are the same as the public nodes connected there.When N-hop-K-anony performs node side substitution, it starts from several evaluation criteria of the nature of social network graph. Finally, the network aggregation coefficient is selected as the theoretical basis to improve the original algorithm. The improved algorithm deals with edge substitution and encodes the improved algorithm. Thirdly, the contrast experiment of the improved algorithm is carried out on the four real and effective data sets of email-Eu-core,College Msg,Facebook and ca-Gr Qc. Through comparison experiments, we can find that the improved algorithm can protect node centrality to some extent, especially tight centrality and medium centrality, in the case of basically consistent time performance, and in the network aggregation coefficient, the improved algorithm can protect node centrality to a certain extent, especially the close-centrality and intermediate-centrality. The improved algorithm also has better experimental results than before.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP309
本文編號：2283812
[Abstract]:With the advent of the Internet and big data era, the Internet has brought great convenience to people, but also make people's privacy protection is threatened to a great extent. It is much easier to collect, integrate, analyze and disseminate user information in the existing network environment than in the period when the speed of data dissemination is not so developed. Therefore, how to protect personal privacy on the Internet has become a hot issue. At present, there are many methods and models of social network privacy protection, among which the most classical is the k-anonymous social network privacy protection algorithm. It requires at least K-1 records to be identical to each identified record in a k- anonymous dataset. Therefore, k-anonymous social network privacy protection algorithm to a certain extent to protect personal privacy. However, the existing k- anonymity technology in privacy protection, all the nodes in the social network are set private, ignoring the existence of a large number of public nodes in the actual network. The identity of these public nodes is public and the attacker can use the connection between them and the private node as the background knowledge to re-identify the private node attack, that is, the Connection Fingerprint (CFP) attack. The original anti-CFP privacy protection algorithm protects the centrality of public nodes well, but there are still some shortcomings, and the nature of social network graph is not considered as much as possible. In this paper, an improved privacy protection algorithm against CFP social networks is proposed. The main work is as follows: first, the original privacy protection algorithm against CFP attack is analyzed. For CFP attacks, the existing privacy protection algorithms of social networks randomly select the private nodes in the equivalent group when implementing edge substitution, ignoring the centrality of each private node in the network diagram. Secondly, an improved privacy protection algorithm (N-hop-K-anony) against CFP attacks is proposed, which is an improved privacy protection algorithm against CFP attacks, that is, K-anony algorithm takes into account the shortcomings of graph properties. The idea is: in the n-hop range, at least the remaining k-1 nodes for any private node v are the same as the public nodes connected there.When N-hop-K-anony performs node side substitution, it starts from several evaluation criteria of the nature of social network graph. Finally, the network aggregation coefficient is selected as the theoretical basis to improve the original algorithm. The improved algorithm deals with edge substitution and encodes the improved algorithm. Thirdly, the contrast experiment of the improved algorithm is carried out on the four real and effective data sets of email-Eu-core,College Msg,Facebook and ca-Gr Qc. Through comparison experiments, we can find that the improved algorithm can protect node centrality to some extent, especially tight centrality and medium centrality, in the case of basically consistent time performance, and in the network aggregation coefficient, the improved algorithm can protect node centrality to a certain extent, especially the close-centrality and intermediate-centrality. The improved algorithm also has better experimental results than before.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP309
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