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  本文選題：位置隱私 + LBS��； 參考：《華東師范大學》2017年碩士論文

【摘要】：近年來,針對位置隱私泄露這一問題,研究者們提出許多新的隱私保護方案。但現(xiàn)有的位置隱私保護技術中很多未考慮訪問概率、地圖數(shù)據(jù)、位置語義等邊信息,這些信息很可能被惡意攻擊者所利用去挖掘用戶的位置隱私。即使有些研究者考慮了邊信息,他們也將邊信息看作是普適的,而不考慮用戶的個性化特征。針對LBS中位置隱私保護問題,本文提出了兩種新的位置隱私保護方法,分別從全局訪問概率和個性化訪問概率出發(fā)選擇位置,從而實現(xiàn)位置k匿名。本文主要工作包括以下幾個方面:·基于全局訪問概率的位置隱私保護方法該方法首先考慮廣大用戶的訪問特性,從大量的用戶訪問數(shù)據(jù)中計算出不同位置的全局訪問概率。然后結合用戶的隱私需求k,查找滿足用戶需求的匿名區(qū)域,隨后計算每個匿名區(qū)域的位置信息熵,從中選出信息熵最大的m個區(qū)域,并在m個區(qū)域中隨機選擇一個作為用戶最終的匿名位置。·基于個性化訪問概率的位置隱私保護方法我們提出了一種基于個性化訪問概率的位置隱私保護方法,即EPLA方法。對于任一用戶,該算法首先根據(jù)其在過去已訪問的位置計算出這些已訪問位置之間距離集合,然后使用核密度估計(Kernel Density Estimation,KDE)計算出用戶訪問位置之間距離的個性化分布,通過該用戶已訪問位置之間距離的個性化分布函數(shù)計算出每個位置錨點的個性化訪問概率。然后我們使用ASS算法選擇合適的候選錨點完成該用戶位置的個性化匿名�！PLA算法優(yōu)化我們根據(jù)核密度估計的計算特征,使用快高斯轉換和高斯分布的3σ規(guī)則對EPLA進行優(yōu)化,提出了一種高效的位置隱私保護算法APLA,該算法對EPLA算法中EPVP算法進行優(yōu)化,很大的改善了 EPLA算法的計算時間開銷。
[Abstract]:In recent years, many new privacy protection schemes have been proposed to solve the problem of location privacy disclosure. However, many existing location privacy protection techniques do not consider access probability, map data, location semantics and other edge information, which may be used by malicious attackers to exploit the location privacy of users. Even if some researchers consider edge information, they regard edge information as universal, regardless of the user's personalized features. Aiming at the problem of location privacy protection in LBS, this paper proposes two new location privacy protection methods, which select location from global access probability and personalized access probability, respectively, so as to realize location k anonymity. The main work of this paper includes the following aspects: the location privacy protection method based on global access probability this method first considers the access characteristics of the majority of users and calculates the global access probability of different locations from a large number of user access data. Then, combining the privacy requirement of the user, we find the anonymous region that meets the user's needs, and then calculate the location information entropy of each anonymous region, and select the m regions with the largest information entropy. A location privacy protection method based on personalized access probability is proposed, which is called EPLA method, which is based on personalized access probability. For any user, the algorithm first calculates the set of distances between these visited locations according to the locations they have visited in the past, and then calculates the personalized distribution of the distance between the user's access locations using Kernel density estimation (KDE). The personalized access probability of each anchor point is calculated by the personalized distribution function of the distance between the visited locations. Then we use the ass algorithm to select the appropriate candidate anchor points to complete the personalized anonymity of the user location. We optimize the Gao Si algorithm according to the computational characteristics of the kernel density estimation, and use the fast Gao Si transformation and the 3 蟽 rule of the Gao Si distribution to optimize the Gao Si. This paper presents an efficient location privacy protection algorithm (APLAA), which optimizes the EPVP algorithm in the EPLA algorithm and greatly improves the computational time cost of the EPLA algorithm.
【學位授予單位】：華東師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP309

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本文編號：2101308