一種引入反饋懲罰機制的個性化數(shù)據(jù)匿名發(fā)布模型
發(fā)布時間：2018-10-22 19:52
【摘要】：為了避免個人隱私信息被泄露,一般數(shù)據(jù)集在發(fā)布時都會進行匿名化脫敏處理,使得攻擊者無法從發(fā)布的數(shù)據(jù)中找到具體個人的隱私信息,從而避免受到名譽、財產(chǎn)或身體方面的損失。在當前的信息時代,作為最常見的個人數(shù)據(jù)發(fā)布場景,基于個人信息的網(wǎng)絡服務就不可避免的成為重災區(qū)。傳統(tǒng)的網(wǎng)絡服務模型只存在服務方和通信網(wǎng)絡提供的隱私保護,但當用戶信息成為商家的掘金地時,用戶的個人信息不免被惡意收集,從而使得用戶成為“無秘之人”,隨時會受到未知源頭的惡意攻擊。本文以隱私保護為切入點,探討了交互式個人數(shù)據(jù)發(fā)布場景下的隱私信息保護及其效用平衡的問題。在綜合分析了各種匿名保護發(fā)布原則后,提出了在傳統(tǒng)隨機博弈的理論框架下引入反饋懲罰機制,并用個性化屬性泄露風險之和低于隱私泄露容忍度對博弈結果進行糾錯的新想法,構造了一個引入反饋懲罰機制的個性化數(shù)據(jù)匿名發(fā)布模型,并用實驗對其效果進行了驗證。該模型基于服務方與用戶之間的服務過程可以抽象為一個混合策略完全信息靜態(tài)博弈,通過求解混合策略納什均衡為用戶選擇最佳的應對策略,始終使用戶獲得最大博弈收益。實驗證明該模型確實對前者進行了有效改善,結論主要體現(xiàn)在兩點:1)文中所提出的模型對具體用戶在數(shù)據(jù)效用率、隱私保護度、模型貢獻率三方面具有穩(wěn)定性,也即,此三者不會隨著用戶服務發(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性化屬性配置有關,不同用戶能得到的數(shù)據(jù)效用率、隱私保護度是不同的,這一方面體現(xiàn)了模型的個性化,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.
【學位授予單位】：湖北師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP309
本文編號：2288148
[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.
【學位授予單位】：湖北師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP309
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