發(fā)布時(shí)間：2018-10-22 19:52

【摘要】：為了避免個(gè)人隱私信息被泄露,一般數(shù)據(jù)集在發(fā)布時(shí)都會(huì)進(jìn)行匿名化脫敏處理,使得攻擊者無(wú)法從發(fā)布的數(shù)據(jù)中找到具體個(gè)人的隱私信息,從而避免受到名譽(yù)、財(cái)產(chǎn)或身體方面的損失。在當(dāng)前的信息時(shí)代,作為最常見(jiàn)的個(gè)人數(shù)據(jù)發(fā)布場(chǎng)景,基于個(gè)人信息的網(wǎng)絡(luò)服務(wù)就不可避免的成為重災(zāi)區(qū)。傳統(tǒng)的網(wǎng)絡(luò)服務(wù)模型只存在服務(wù)方和通信網(wǎng)絡(luò)提供的隱私保護(hù),但當(dāng)用戶信息成為商家的掘金地時(shí),用戶的個(gè)人信息不免被惡意收集,從而使得用戶成為“無(wú)秘之人”,隨時(shí)會(huì)受到未知源頭的惡意攻擊。本文以隱私保護(hù)為切入點(diǎn),探討了交互式個(gè)人數(shù)據(jù)發(fā)布場(chǎng)景下的隱私信息保護(hù)及其效用平衡的問(wèn)題。在綜合分析了各種匿名保護(hù)發(fā)布原則后,提出了在傳統(tǒng)隨機(jī)博弈的理論框架下引入反饋懲罰機(jī)制,并用個(gè)性化屬性泄露風(fēng)險(xiǎn)之和低于隱私泄露容忍度對(duì)博弈結(jié)果進(jìn)行糾錯(cuò)的新想法,構(gòu)造了一個(gè)引入反饋懲罰機(jī)制的個(gè)性化數(shù)據(jù)匿名發(fā)布模型,并用實(shí)驗(yàn)對(duì)其效果進(jìn)行了驗(yàn)證。該模型基于服務(wù)方與用戶之間的服務(wù)過(guò)程可以抽象為一個(gè)混合策略完全信息靜態(tài)博弈,通過(guò)求解混合策略納什均衡為用戶選擇最佳的應(yīng)對(duì)策略,始終使用戶獲得最大博弈收益。實(shí)驗(yàn)證明該模型確實(shí)對(duì)前者進(jìn)行了有效改善,結(jié)論主要體現(xiàn)在兩點(diǎn):1)文中所提出的模型對(duì)具體用戶在數(shù)據(jù)效用率、隱私保護(hù)度、模型貢獻(xiàn)率三方面具有穩(wěn)定性,也即,此三者不會(huì)隨著用戶服務(wù)發(fā)起次數(shù)的改變而改變,這體現(xiàn)了模型本身的穩(wěn)定性。2)該模型的使用效果與用戶本身的個(gè)性化屬性配置有關(guān),不同用戶能得到的數(shù)據(jù)效用率、隱私保護(hù)度是不同的,這一方面體現(xiàn)了模型的個(gè)性化,另一方面也能最大程度保證數(shù)據(jù)效用與隱私保護(hù)之間的平衡。
[Abstract]:In order to avoid the disclosure of personal privacy information, the general data set is desensitized anonymously when it is published, which makes it impossible for an attacker to find the privacy information of a specific individual from the published data, thus avoiding being reputed. Loss of property or body In the current information age, as the most common personal data release scenario, the network service based on personal information will inevitably become a disaster area. The traditional network service model has only the privacy protection provided by the service side and the communication network, but when the user information becomes the gold mine of the merchant, the personal information of the user is collected maliciously, which makes the user become the "unsecretive person". At any time will be the unknown source of malicious attacks. Based on privacy protection, this paper discusses privacy information protection and its utility balance in interactive personal data publishing scenarios. After a comprehensive analysis of various anonymous protection release principles, a feedback penalty mechanism is proposed under the framework of traditional stochastic game theory. Based on the new idea that the sum of the risk of personalized attribute leakage is lower than the tolerance of privacy disclosure, a new idea of correcting the result of game is proposed, and a model of anonymous publication of personalized data with feedback penalty mechanism is constructed, and its effect is verified by experiments. This model can be abstracted as a mixed strategy complete information static game based on the service process between the service party and the user. By solving the Nash equilibrium of the mixed strategy to select the best coping strategy for the user, the user can always obtain the maximum benefit of the game. Experimental results show that the model can effectively improve the former. The conclusions are as follows: 1) the proposed model is stable to specific users in three aspects: data utility rate, privacy protection, and model contribution rate, that is, the proposed model is stable in terms of data utility rate, privacy protection degree and model contribution rate. These three do not change with the number of user service initiation, which reflects the stability of the model itself. 2) the use of the model is related to the user's own personalized property configuration, different users can get the data utility rate. The degree of privacy protection is different, which reflects the individuation of the model, on the other hand, it can ensure the balance between data utility and privacy protection.
【學(xué)位授予單位】：湖北師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP309

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