位置社交網(wǎng)絡的服務推薦與隱私保護研究
發(fā)布時間：2018-09-07 18:49
【摘要】：近年來,大量傳感器嵌入的智能移動設備的廣泛應用,促進了位置社交網(wǎng)絡的快速發(fā)展。用戶能夠通過移動終端在任何時間、任何地點訪問位置社交網(wǎng)絡,以獲取位置相關的服務。并且,位置社交網(wǎng)絡中的用戶利用現(xiàn)有的定位技術(例如:GPS、Wi-Fi或者RFID等),可以互相分享感興趣的帶地理位置信息的用戶社交媒體。隨著大數(shù)據(jù)時代的到來,海量的服務被發(fā)送到服務代理,以提供給用戶不同功能或者性能的服務選項。由于傳統(tǒng)服務檢索機制以及通信模式的限制,一方面導致服務請求者無法快速、準確地從這些海量服務中挑選出所需要的服務;另一方面,對于一些新的服務,因為缺乏一定的服務描述信息,所以無法被合適的用戶獲取。服務推薦系統(tǒng)能夠根據(jù)用戶的歷史訪問記錄,為用戶主動推送服務。但是,現(xiàn)存的位置社交網(wǎng)絡下的服務推薦方法缺乏對用戶生活習慣以及行為偏好的考慮,無法將滿足其個性化需求的服務推薦給用戶。此外,用戶在享受位置社交網(wǎng)絡帶來的巨大便利的同時,不可避免地會遭受個人隱私泄露的風險。位置社交網(wǎng)絡中,用戶需要將真實的位置信息上傳到位置社交網(wǎng)絡服務器,以獲取個性化服務體驗。但是,攻擊者能夠利用位置社交網(wǎng)絡服務器所具有的“誠實而好奇”特點,竊取到受害者真實的位置數(shù)據(jù),并通過背景知識信息,推理出受害者的生活模式、行為偏好以及未來將要訪問的位置等,從而對用戶的人身安全造成威脅。本文重點圍繞位置社交網(wǎng)絡中如何為用戶推薦滿足其需求的個性化服務、如何保護用戶的個人隱私以及如何在隱私保護的情況下實現(xiàn)個性化服務推薦展開深入研究。主要工作體現(xiàn)在服務推薦機制與隱私保護兩個方面,包括:(1)位置社交網(wǎng)絡的相似用戶發(fā)現(xiàn);(2)位置社交網(wǎng)絡的興趣點服務推薦;(3)位置社交網(wǎng)絡的軌跡隱私保護以及(4)隱私保護的個性化服務推薦四個研究點,形成了一套服務推薦與隱私保護基礎研究體系。具體研究工作如下:(1)在位置社交網(wǎng)絡的相似用戶發(fā)現(xiàn)方面,針對現(xiàn)有的相似用戶查找方法缺乏對用戶偏好的考慮,研究了一種基于移動軌跡模式的潛在好友發(fā)現(xiàn)方法。通過分析原始軌跡數(shù)據(jù)的特點以及分布情況,設計了兩種聚類算法。同時,提出了一種基于TF-IDF的位置分類方法,生成語義信息,構建用戶的移動軌跡模式。通過考慮活動序列以及類型流行度,發(fā)現(xiàn)潛在的相似用戶。這部分內(nèi)容有效解決了數(shù)據(jù)稀疏性問題,同時提高用戶的服務體驗質(zhì)量,為未來位置社交網(wǎng)絡中個性化服務推薦提供必要的支撐技術。(2)在位置社交網(wǎng)絡的興趣點服務推薦方面,針對現(xiàn)有的興趣點服務推薦方法中存在的位置有限性與數(shù)據(jù)稀疏性問題,研究了 一種模式與偏好感知的興趣點服務推薦方法。利用用戶與興趣點之間的關系,將地理位置信息轉(zhuǎn)換為語義位置信息,通過考慮位置流行度與用戶熟悉度,構建用戶偏好模型。然后,提出了一種模式提取算法,有效提取出用戶的移動行為模式,匹配每個用戶的移動行為模式,為目標用戶挖掘出符合其偏好的候選服務。最終,設計了一種打分機制,從而為目標用戶推薦前k個候選服務。這部分內(nèi)容利用移動軌跡描述方法有效地反映了用戶的興趣及偏好,提高了服務系統(tǒng)的可擴展性,為未來位置社交網(wǎng)絡中個性化興趣點服務推薦提供有益的解決思路。(3)在位置社交網(wǎng)絡的軌跡隱私保護方面,針對傳統(tǒng)軌跡隱私保護方法存在的用戶敏感信息泄露、數(shù)據(jù)可用性低以及缺乏自適應等問題,研究了一種偏好感知的軌跡隱私保護方法。將用戶移動軌跡中的原始點重構為停留區(qū)域與位置區(qū)域,構建位置匿名空間。設計了一種隱私風險評級方法,根據(jù)用戶對位置的偏好不同,采取不同的位置匿名機制,將匿名后的位置連接起來,以生成匿名的軌跡序列。這部分內(nèi)容能夠在保護用戶個人隱私不被泄露的情況下,提高數(shù)據(jù)的可用性,為未來位置社交網(wǎng)絡中自適應隱私保護提供有效的解決方案。(4)在隱私保護的個性化服務推薦方面,針對現(xiàn)有的軌跡隱私保護方法缺乏考慮個性化服務推薦與隱私保護之間的均衡問題,研究了一種差分隱私保護的潛在軌跡社區(qū)發(fā)現(xiàn)方法。利用軌跡分段技術,將原始的軌跡序列分為若干個不同的軌跡段。同時,設計了位置泛化矩陣以及軌跡序列函數(shù),以泛化原始的位置點與軌跡段。在位置泛化矩陣生成與軌跡序列函數(shù)生成階段,分別將拉普拉斯分布的噪聲與指數(shù)分布的噪聲添加到輸出結果中,以使提出的最優(yōu)泛化軌跡序列選擇算法滿足∈-差分隱私。位置社交網(wǎng)絡服務器接收到泛化的軌跡序列,通過考慮地理距離與語義距離,將軌跡序列聚類為社區(qū),發(fā)現(xiàn)具有相似行為偏好的用戶,以完成個性化服務推薦。這部分內(nèi)容有效地平衡了個性化服務推薦與隱私保護,為未來位置社交網(wǎng)絡中隱私保護下的服務推薦提供可行的技術方案。本文以位置社交網(wǎng)絡的服務推薦與隱私保護為核心,從服務推薦、隱私保護以及支持隱私保護的服務推薦三方面展開系統(tǒng)研究,并有機地聯(lián)系起來,以構成一整套研究體系,輸出一些基礎研究成果,具有一定的創(chuàng)新性。在研究方法上,本文采用相關工作調(diào)研、數(shù)學建模、算法設計以及實驗分析等一系列研究方法。在數(shù)據(jù)分析上,本文基于真實數(shù)據(jù)集,考慮了位置社交網(wǎng)絡下服務推薦與隱私保護的實際性能指標,驗證所提方法的優(yōu)越性。本文取得的研究成果對未來位置社交網(wǎng)絡的研究與發(fā)展具有一定的借鑒意義。
[Abstract]:In recent years, the widespread use of smart mobile devices embedded with a large number of sensors has promoted the rapid development of location-based social networks. Users can access location-based social networks through mobile terminals at any time and anywhere to obtain location-related services. Moreover, users in location-based social networks make use of existing location-based technologies (e.g. GP) S, Wi-Fi, RFID, etc.) can share interesting social media with geographic location information. With the advent of the big data era, a large number of services are sent to service agents to provide users with different functions or performance of service options. Service requesters can not quickly and accurately select the required services from these massive services; on the other hand, for some new services, because of the lack of certain service description information, it can not be accessed by the appropriate users. The existing service recommendation methods in location-based social networks lack the consideration of users'living habits and behavior preferences, so they can not recommend services that meet their personalized needs to users. In a network, users need to upload real location information to a location-based social network server for personalized service experience. However, attackers can take advantage of the "honest and curious" characteristics of a location-based social network server to steal the victim's real location data and infer the victim's identity through background information. This paper focuses on how to recommend personalized services to meet users'needs in location-based social networks, how to protect users' privacy and how to implement personalized service recommendation under privacy protection. The main work includes: (1) similarity user discovery in location-based social networks; (2) interest point service recommendation in location-based social networks; (3) trajectory privacy protection in location-based social networks; and (4) personalized service recommendation in privacy protection. A basic research system of service recommendation and privacy protection is proposed. The main research work is as follows: (1) In the aspect of similarity user discovery in location-based social networks, a potential friend discovery method based on mobile trajectory pattern is studied for the lack of consideration of user preference in existing similar user search methods. At the same time, a location classification method based on TF-IDF is proposed to generate semantic information and construct the user's trajectory pattern. Potential similar users are found by considering the activity sequence and type popularity. This part of content effectively solves the problem of data sparsity and improves the performance. The quality of user service experience provides the necessary support technology for personalized service recommendation in future location-based social networks. (2) In the aspect of interest point service recommendation in location-based social networks, aiming at the problems of location limitation and data sparsity in the existing interest point service recommendation methods, this paper studies the development of a pattern and preference perception. User preference model is constructed by transforming geographic location information into semantic location information and considering location popularity and user familiarity. Then, a pattern extraction algorithm is proposed to effectively extract user's mobile behavior patterns and match each user's mobile line. Finally, a scoring mechanism is designed to recommend the first k candidate services for the target users. This part effectively reflects the interests and preferences of the users, improves the scalability of the service system, and provides a future location-based social network. (3) For the trajectory privacy protection of location-based social networks, a preference-aware trajectory privacy protection method is proposed to solve the problems of user-sensitive information leakage, low data availability and lack of self-adaptation in traditional trajectory privacy protection methods. The original point in the trajectory is reconstructed into the residence area and the location area to construct the location anonymity space. A privacy risk rating method is designed. According to the different preferences of the users, different location anonymity mechanisms are adopted to connect the anonymous locations to generate the anonymous trajectory sequence. (4) In the aspect of personalized service recommendation for privacy protection, the existing trajectory privacy protection methods do not consider the balance between personalized service recommendation and privacy protection. A potential trajectory community discovery method for differential privacy preservation is studied. The original trajectory sequence is divided into several different trajectory segments by using the trajectory segmentation technique. At the same time, the position generalization matrix and the trajectory sequence function are designed to generalize the original position points and trajectory segments. In the generation phase, the Laplace distribution noise and the exponential distribution noise are added to the output results respectively to satisfy the < - differential privacy of the proposed optimal generalized trajectory sequence selection algorithm. This part effectively balances personalized service recommendation and privacy protection, and provides feasible technical solutions for service recommendation under privacy protection in future location-based social networks. This paper focuses on service recommendation and privacy protection in location-based social networks. In order to form a complete research system and output some basic research results, this paper has a certain degree of innovation. In the research method, this paper uses related research, mathematical modeling, algorithm design and experimental points. Based on the real data set, this paper considers the actual performance indicators of service recommendation and privacy protection in location-based social networks, and verifies the superiority of the proposed method.
【學位授予單位】：北京郵電大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TP391.3;TP309
本文編號：2229119
[Abstract]:In recent years, the widespread use of smart mobile devices embedded with a large number of sensors has promoted the rapid development of location-based social networks. Users can access location-based social networks through mobile terminals at any time and anywhere to obtain location-related services. Moreover, users in location-based social networks make use of existing location-based technologies (e.g. GP) S, Wi-Fi, RFID, etc.) can share interesting social media with geographic location information. With the advent of the big data era, a large number of services are sent to service agents to provide users with different functions or performance of service options. Service requesters can not quickly and accurately select the required services from these massive services; on the other hand, for some new services, because of the lack of certain service description information, it can not be accessed by the appropriate users. The existing service recommendation methods in location-based social networks lack the consideration of users'living habits and behavior preferences, so they can not recommend services that meet their personalized needs to users. In a network, users need to upload real location information to a location-based social network server for personalized service experience. However, attackers can take advantage of the "honest and curious" characteristics of a location-based social network server to steal the victim's real location data and infer the victim's identity through background information. This paper focuses on how to recommend personalized services to meet users'needs in location-based social networks, how to protect users' privacy and how to implement personalized service recommendation under privacy protection. The main work includes: (1) similarity user discovery in location-based social networks; (2) interest point service recommendation in location-based social networks; (3) trajectory privacy protection in location-based social networks; and (4) personalized service recommendation in privacy protection. A basic research system of service recommendation and privacy protection is proposed. The main research work is as follows: (1) In the aspect of similarity user discovery in location-based social networks, a potential friend discovery method based on mobile trajectory pattern is studied for the lack of consideration of user preference in existing similar user search methods. At the same time, a location classification method based on TF-IDF is proposed to generate semantic information and construct the user's trajectory pattern. Potential similar users are found by considering the activity sequence and type popularity. This part of content effectively solves the problem of data sparsity and improves the performance. The quality of user service experience provides the necessary support technology for personalized service recommendation in future location-based social networks. (2) In the aspect of interest point service recommendation in location-based social networks, aiming at the problems of location limitation and data sparsity in the existing interest point service recommendation methods, this paper studies the development of a pattern and preference perception. User preference model is constructed by transforming geographic location information into semantic location information and considering location popularity and user familiarity. Then, a pattern extraction algorithm is proposed to effectively extract user's mobile behavior patterns and match each user's mobile line. Finally, a scoring mechanism is designed to recommend the first k candidate services for the target users. This part effectively reflects the interests and preferences of the users, improves the scalability of the service system, and provides a future location-based social network. (3) For the trajectory privacy protection of location-based social networks, a preference-aware trajectory privacy protection method is proposed to solve the problems of user-sensitive information leakage, low data availability and lack of self-adaptation in traditional trajectory privacy protection methods. The original point in the trajectory is reconstructed into the residence area and the location area to construct the location anonymity space. A privacy risk rating method is designed. According to the different preferences of the users, different location anonymity mechanisms are adopted to connect the anonymous locations to generate the anonymous trajectory sequence. (4) In the aspect of personalized service recommendation for privacy protection, the existing trajectory privacy protection methods do not consider the balance between personalized service recommendation and privacy protection. A potential trajectory community discovery method for differential privacy preservation is studied. The original trajectory sequence is divided into several different trajectory segments by using the trajectory segmentation technique. At the same time, the position generalization matrix and the trajectory sequence function are designed to generalize the original position points and trajectory segments. In the generation phase, the Laplace distribution noise and the exponential distribution noise are added to the output results respectively to satisfy the < - differential privacy of the proposed optimal generalized trajectory sequence selection algorithm. This part effectively balances personalized service recommendation and privacy protection, and provides feasible technical solutions for service recommendation under privacy protection in future location-based social networks. This paper focuses on service recommendation and privacy protection in location-based social networks. In order to form a complete research system and output some basic research results, this paper has a certain degree of innovation. In the research method, this paper uses related research, mathematical modeling, algorithm design and experimental points. Based on the real data set, this paper considers the actual performance indicators of service recommendation and privacy protection in location-based social networks, and verifies the superiority of the proposed method.
【學位授予單位】：北京郵電大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TP391.3;TP309
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