本文選題：LBS + 近鄰檢測�。� 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：近鄰檢測是基于位置服務(wù)LBS(Location Based Service)中的重要功能,能夠搜尋附近用戶,該功能廣泛應(yīng)用于社交、商業(yè)、軍事等各領(lǐng)域。現(xiàn)如今人們在享受位置服務(wù)的同時也更加重視隱私保護(hù)。由于近鄰檢測中搜尋附近好友的原理是基于對用戶的定位,因此很容易涉及到位置信息等隱私的泄漏。因此多數(shù)近鄰檢測算法為保護(hù)隱私而大大增加了算法的時間復(fù)雜度,影響了檢測效率。本文的研究目的是在保護(hù)用戶位置隱私的前提下提高近鄰檢測算法的效率,提高服務(wù)的實(shí)用性。本文利用歐氏空間在LBS中能夠模擬在時空數(shù)據(jù)庫的實(shí)際情況且方便理論論證的優(yōu)點(diǎn),提出了歐式空間和公路網(wǎng)絡(luò)模型下的近鄰檢測算法。本文綜合考慮了歐氏空間和公路網(wǎng)絡(luò)兩種距離模型在理想環(huán)境和實(shí)際應(yīng)用中相互結(jié)合的特點(diǎn),發(fā)揮其各自優(yōu)勢。另外,本文最大程度地實(shí)現(xiàn)了用戶位置隱私的保護(hù)。首先利用歐式空間的特點(diǎn)提出了基于隱私保護(hù)的近鄰檢測算法。該算法對被檢測對象進(jìn)行基于歐式空間的匿名方式處理從而保護(hù)其位置隱私,此外通過構(gòu)造Voronoi單元對檢測區(qū)域進(jìn)行分割,且對可能結(jié)果集進(jìn)行條件篩選來縮小檢測的區(qū)域,從而提高了檢測效率。之后通過仿真實(shí)驗(yàn)的具體數(shù)據(jù)對該算法與傳統(tǒng)的近鄰檢測算法,就檢測準(zhǔn)確率、耗時情況等各項(xiàng)性能進(jìn)行了對比。結(jié)果表明該算法在檢測效率方面優(yōu)于傳統(tǒng)近鄰檢測算法,且能夠有效地保護(hù)被檢測對象的位置隱私。然后在此算法基礎(chǔ)之上,針對公路網(wǎng)絡(luò)能夠更有效模擬實(shí)際對象的空間位置關(guān)系的特點(diǎn),提出了基于隱私保護(hù)的公路網(wǎng)絡(luò)近鄰檢測算法,對被檢測對象進(jìn)行基于公路網(wǎng)絡(luò)的匿名方式處理防止隱私泄露。該算法通過擴(kuò)展圓對二維檢測平面進(jìn)行分割,根據(jù)被檢測對象在子區(qū)域中的分布情況進(jìn)行篩選,再將被檢測對象是其最近鄰點(diǎn)的可能性值與既定閾值進(jìn)行比較進(jìn)一步過濾,最終檢測出近鄰點(diǎn)。此外通過仿真實(shí)驗(yàn)對該算法進(jìn)行了數(shù)據(jù)測試,并且將結(jié)果與歐式空間近鄰檢測算法的性能進(jìn)行對比,發(fā)現(xiàn)公路網(wǎng)絡(luò)算法在效率和精確率方面都具有更好的優(yōu)勢。以上兩種算法均較為有效地實(shí)現(xiàn)了檢測對象的位置隱私保護(hù)。
[Abstract]:Nearest neighbor detection is an important function of location based LBS(Location Based Service, which can search nearby users. It is widely used in social, commercial, military and other fields. Nowadays, people pay more attention to privacy while enjoying location services. Because the principle of searching for close friends in nearest neighbor detection is based on the location of the user, it is easy to leak the privacy such as location information. Therefore, most nearest neighbor detection algorithms greatly increase the time complexity and affect the detection efficiency in order to protect privacy. The purpose of this paper is to improve the efficiency of nearest neighbor detection algorithm and improve the practicability of service under the premise of protecting user location privacy. Based on the advantage of Euclidean space being able to simulate the actual situation in spatio-temporal database in LBS and convenient for theoretical argumentation, this paper presents an algorithm for nearest neighbor detection in Euclidean space and highway network model. In this paper, the characteristics of Euclidean space and highway network models are considered, which are combined in ideal environment and practical application, and their respective advantages are brought into play. In addition, this paper maximizes the protection of user location privacy. Firstly, a privacy protection based nearest neighbor detection algorithm is proposed based on the characteristics of Euclidean space. In order to protect the privacy of the detected object, the detected object is processed anonymously based on Euclidean space. In addition, the detected region is segmented by constructing a Voronoi unit, and the detected region is reduced by conditional filtering of the possible result set. Thus, the detection efficiency is improved. Then the performance of the algorithm is compared with that of the traditional nearest neighbor detection algorithm, such as detection accuracy, time consuming and so on. The results show that the proposed algorithm is superior to the traditional nearest neighbor detection algorithm in detection efficiency and can effectively protect the location privacy of the object under detection. On the basis of this algorithm, aiming at the characteristic that highway network can more effectively simulate the spatial position relationship of real objects, a privacy protection based nearest neighbor detection algorithm for highway network is proposed. The detected object is treated anonymously based on highway network to prevent privacy disclosure. In this algorithm, the two-dimensional detection plane is segmented by extending the circle, and then the probability value of the nearest neighbor of the detected object is filtered further by comparing the probability value of the detected object with the established threshold value according to the distribution of the detected object in the sub-region. Finally, the nearest neighbor point is detected. In addition, the algorithm is tested by simulation, and compared with the performance of Euclidean spatial nearest neighbor detection algorithm, it is found that the highway network algorithm has better efficiency and accuracy. The above two algorithms can effectively protect the location privacy of the detected object.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN929.5

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