本文選題：隱私保護(hù)　切入點(diǎn)：背景知識攻擊　出處：《華中科技大學(xué)》2016年碩士論文

【摘要】：隨著各種社交網(wǎng)絡(luò)、個性化推薦等服務(wù)的發(fā)展,個人信息往往被服務(wù)提供者收集、管理并加以利用,由此也產(chǎn)生了個人信息被泄漏的風(fēng)險�，F(xiàn)有的個人信息隱私保護(hù)方法,在具有各種背景知識的攻擊者面前面臨著更加嚴(yán)峻的挑戰(zhàn)。因此,研究和改進(jìn)隱私保護(hù)方法以適應(yīng)新的攻擊場景具有重要意義。針對目前的隱私保護(hù)方法對已知個體相關(guān)性進(jìn)行攻擊的場景研究不足的問題,以一種比較常見的已知個體相關(guān)性的攻擊場景為背景,設(shè)計了一種能夠抵抗該攻擊的隱私保護(hù)模型,即r-抗元組關(guān)系攻擊隱私保護(hù)模型。為了得到該隱私保護(hù)模型,首先提取出該攻擊場景的本質(zhì)并抽象成已知元組關(guān)系的攻擊模型,并對攻擊模型中的個體間關(guān)系進(jìn)行范圍的界定和建模;然后給出能夠抵抗該攻擊模型的元組應(yīng)該滿足的條件即r-抗元組關(guān)系攻擊性,約束匿名后的關(guān)聯(lián)元組的候選敏感屬性集合之間的交集大小至少為閾值r;最后根據(jù)是否包含關(guān)聯(lián)元組為不同類型的分組分別施加不同程度的隱私約束,給出抗元組關(guān)系攻擊隱私保護(hù)模型的定義,并從理論上證明模型的安全性。以r-抗元組關(guān)系攻擊隱私保護(hù)模型為基礎(chǔ),設(shè)計出用于生成匿名數(shù)據(jù)集的算法,包括用于提取數(shù)據(jù)集中背景知識的敏感屬性等值關(guān)系提取算法以及用于生成滿足安全約束的匿名發(fā)布表的抗元組關(guān)系攻擊隱私保護(hù)算法(包括分組創(chuàng)建算法、分組補(bǔ)充算法和表分割三個部分),并給出算法正確性、安全性、可用性以及代價的理論分析。實(shí)驗(yàn)表明,滿足r-抗元組關(guān)系攻擊隱私保護(hù)模型的隱私保護(hù)算法生成的匿名數(shù)據(jù)與滿足?-多樣性的Anatomy算法生成的匿名數(shù)據(jù)相比,兩者不僅具有相近的可用性,而且前者具有更好的安全性。
[Abstract]:With the development of various social networks, personalized recommendation and other services, personal information is often collected, managed and utilized by service providers. There are even more serious challenges facing attackers with a variety of backgrounds. It is of great significance to study and improve privacy protection methods to adapt to new attack scenarios. Based on a common attack scenario with known individual correlation, this paper designs a privacy protection model that can resist this attack, namely r-tuple relation attack privacy protection model, in order to obtain the privacy protection model. Firstly, the essence of the attack scene is extracted and abstracted into an attack model with known tuple relationships, and the scope of the relationship between individuals in the attack model is defined and modeled. Then, the condition that the tuple can resist the attack model is given, that is, r-anti-tuple relation aggression. The size of the intersection between candidate sensitive attribute sets after constrained anonymous tuples is at least a threshold r; finally, privacy constraints are imposed to varying degrees depending on whether groups containing association tuples are of different types. This paper gives the definition of privacy protection model against tuple relation attack, and proves the security of the model theoretically. Based on the privacy protection model of r-tuple relation attack, an algorithm is designed to generate anonymous dataset. It includes a sensitive attribute equivalence extraction algorithm for extracting background knowledge in a dataset and an anti-tuple relational attack privacy protection algorithm for generating anonymous publishing tables that meet security constraints (including grouping creation algorithm). The theoretical analysis of the correctness, security, availability and cost of the algorithm is given. The experimental results show that, The anonymous data and satisfaction generated by privacy protection algorithm satisfying r-tuple relation attack privacy protection model? Compared with the anonymous data generated by the diversity Anatomy algorithm, the two methods not only have similar availability, but also have better security.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP309

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