【摘要】：移動(dòng)互聯(lián)網(wǎng)、社交網(wǎng)絡(luò)愈發(fā)深刻地融入人們的日常生活,特別是在發(fā)展勢(shì)頭猛烈的物聯(lián)網(wǎng)、多樣化的個(gè)性化服務(wù)的相互作用下,越來(lái)越多的社交用戶的隱私信息被主動(dòng)或無(wú)意地暴露在網(wǎng)絡(luò)環(huán)境中。另外,大量的社交網(wǎng)絡(luò)數(shù)據(jù),為網(wǎng)絡(luò)應(yīng)用提供商帶來(lái)更多利益的同時(shí),也為惡意攻擊者提供了動(dòng)機(jī)。因此,社交網(wǎng)絡(luò)環(huán)境下,針對(duì)用戶信息的隱私保護(hù)問(wèn)題的研究具有重大的理論和現(xiàn)實(shí)意義。近年來(lái),研究人員針對(duì)社交網(wǎng)絡(luò)中的隱私保護(hù)問(wèn)題,提出了多種匿名模型和算法,但是鑒于匿名算法為了實(shí)現(xiàn)隱私保護(hù),需要對(duì)社交網(wǎng)絡(luò)數(shù)據(jù)產(chǎn)生不同程度的干擾,從而降低服務(wù)提供商對(duì)用戶數(shù)據(jù)挖掘分析的準(zhǔn)確度,使用戶的服務(wù)體驗(yàn)大打折扣�？梢�(jiàn),隱私安全與數(shù)據(jù)的可用性是社交網(wǎng)絡(luò)環(huán)境下隱私保護(hù)問(wèn)題研究的兩個(gè)相互矛盾的目標(biāo),研究隱私安全和可用性之間的權(quán)衡成為一個(gè)重要和具有挑戰(zhàn)性的問(wèn)題。另外,相對(duì)于信息隱私安全方面的研究,數(shù)據(jù)效用的研究尚不成熟,目前還沒(méi)有標(biāo)準(zhǔn)的可供普遍使用的數(shù)據(jù)效用損失度量標(biāo)準(zhǔn)。因此,本文以社交網(wǎng)絡(luò)中用戶屬性為研究對(duì)象,致力于研究在確保用戶信息隱私安全的前提下能夠有效降低數(shù)據(jù)效用損失的匿名模型和算法,研究?jī)?nèi)容主要包括以下三部分:(1)針對(duì)目前社交網(wǎng)絡(luò)場(chǎng)景下的隱私保護(hù)問(wèn)題,現(xiàn)有的匿名算法一般以邊或節(jié)點(diǎn)的修改數(shù)量作為評(píng)估匿名數(shù)據(jù)效用損失的唯一標(biāo)準(zhǔn),這些算法隱私保護(hù)程度較高,但是由于忽略了不同的邊或節(jié)點(diǎn)的修改對(duì)社交網(wǎng)絡(luò)結(jié)構(gòu)具有不同的影響,容易導(dǎo)致過(guò)量的數(shù)據(jù)效用損失,影響匿名數(shù)據(jù)的利用價(jià)值�？紤]到這一問(wèn)題,本文從數(shù)據(jù)效用具有結(jié)構(gòu)相似性和信息完整性兩個(gè)方面的角度出發(fā),設(shè)計(jì)了一種更全面的數(shù)據(jù)效用衡量方法UL(G,G’),該方法綜合評(píng)估匿名操作對(duì)網(wǎng)絡(luò)結(jié)構(gòu)和數(shù)據(jù)內(nèi)容兩方面的影響,其評(píng)估標(biāo)準(zhǔn)較以往僅以人為更改操作數(shù)量衡量效用損失的標(biāo)準(zhǔn)更高,降低了匿名數(shù)據(jù)的效用損失。(2)為了解決社交用戶信息隱私安全問(wèn)題,我們改進(jìn)k-度-l-多樣性匿名模型,提出了基于節(jié)點(diǎn)分裂的差異化隱私保護(hù)匿名模型((d,k,l)-u匿名模型),并基于該模型設(shè)計(jì)了相應(yīng)的屬性差異化匿名算法,該算法根據(jù)敏感度函數(shù),將敏感屬性的屬性值劃分到高、中、低三個(gè)隱私匿名組中,并對(duì)不同的匿名組采用不同程度的匿名規(guī)則。該算法將隱私保護(hù)對(duì)象由屬性類精確到具體的屬性值,并通過(guò)差異化匿名降低了匿名數(shù)據(jù)的效用損失,并通過(guò)模擬實(shí)驗(yàn),驗(yàn)證了該算法的有效性。(3)針對(duì)匿名算法對(duì)網(wǎng)絡(luò)結(jié)構(gòu)的干擾,考慮到社交網(wǎng)絡(luò)中的用戶節(jié)點(diǎn)對(duì)網(wǎng)絡(luò)的整體結(jié)構(gòu)具有不同的影響力,即相對(duì)于普通節(jié)點(diǎn),對(duì)“橋節(jié)點(diǎn)”等關(guān)鍵節(jié)點(diǎn)的分裂操作,會(huì)大幅度更改網(wǎng)絡(luò)結(jié)構(gòu)的整體特性,因此,本文對(duì)(d,k,l)-u屬性差異化算法進(jìn)行優(yōu)化,進(jìn)而提出了一種節(jié)點(diǎn)差異化的匿名算法(DKDLD-U匿名算法),該算法引入社會(huì)網(wǎng)路分析中的關(guān)鍵節(jié)點(diǎn)分析,將節(jié)點(diǎn)分為重要節(jié)點(diǎn)和普通節(jié)點(diǎn)兩類,并對(duì)兩類節(jié)點(diǎn)分別采用敏感屬性值泛化和節(jié)點(diǎn)分裂兩種匿名操作,以減少對(duì)網(wǎng)絡(luò)結(jié)構(gòu)的擾動(dòng),提高發(fā)布數(shù)據(jù)的效用。算法的仿真實(shí)驗(yàn)表明,該算法能夠在保證隱私安全的同時(shí),有效降低匿名數(shù)據(jù)的效用損失。
[Abstract]:Mobile Internet and social networks are becoming more and more deeply integrated into people's daily lives, especially with the interaction of dynamic Internet of Things and diversified personalized services, and more and more social users' privacy information is actively or unintentionally exposed to the network environment. In addition, a large amount of social network data provides a motive for a malicious attacker while bringing more benefits to the network application provider. Therefore, in the social network environment, the research on the privacy protection of the user information has great theoretical and practical significance. In recent years, the researchers put forward a variety of anonymous models and algorithms for privacy protection in social networks, but in view of the anonymity algorithm in order to realize the privacy protection, it is necessary to generate different degree of interference to the social network data. so that the accuracy of the service provider to the user data mining analysis is reduced, and the service experience of the user is greatly reduced. It can be seen that the security of privacy and the availability of data are two conflicting goals of the study of privacy protection under the social network environment, and the trade-off between privacy security and availability becomes an important and challenging issue. In addition, the research on the safety of information privacy, the research of data utility is not mature, and there is no standard of data utility loss measurement standard that can be widely used at present. Therefore, on the premise of ensuring the privacy and security of the user's information, this paper is devoted to the study of the anonymous model and the algorithm which can effectively reduce the loss of data utility under the premise of ensuring the privacy and safety of the user, and the research contents mainly include the following three parts: (1) Aiming at the privacy protection problem in the current social network scene, the existing anonymous algorithm generally takes the modified quantity of an edge or a node as the only standard for evaluating the utility loss of the anonymous data, and the privacy protection degree of the algorithms is high, However, due to the fact that the different edges or the modification of the nodes have different influences on the social network structure, the excessive data utility loss can be easily caused, and the utilization value of the anonymous data is affected. In view of this problem, a more comprehensive method of data utility measurement (UL (G, G '), the method comprehensively evaluates the influence of the anonymous operation on the network structure and the data content, and the evaluation standard of the method is higher than that of the prior art only by man-made change operation quantity, and the utility loss of the anonymous data is reduced. (2) In order to solve the security problem of social user information privacy, we improve the k-degree-l-diversity anonymous model, and propose a differential privacy protection anonymous model based on node division ((d, k, l)-u anonymous model). and a corresponding attribute differentiation anonymity algorithm is designed based on the model, and the algorithm divides the attribute value of the sensitive attribute into the high, middle and low privacy anonymous groups according to the sensitivity function, and adopts the different degree of anonymous rules for different anonymous groups. In this algorithm, the privacy protection object is defined by the attribute class to the specific attribute value, and the utility loss of the anonymous data is reduced by the differential anonymity, and the validity of the algorithm is verified through the simulation experiment. (3) Aiming at the interference of the anonymous algorithm to the network structure, the user node in the social network has different influence on the whole structure of the network, that is, the split operation of the key nodes such as the 鈥渂ridge node鈥，

本文編號(hào)：2376895