移动社交网络中位置隐私保护技术研究

发布时间：2018-10-29 15:43

【摘要】：随着移动互联网的飞速发展,移动社交网络用户在逐年大幅增长,越来越多的用户习惯于享受基于移动定位的应用带来的服务。传统的社交网络已经与移动互联网紧密结合在一起,形成了移动、社交、位置服务融合的移动社交环境。基于位置的社交应用为人们生活和工作带来更多的乐趣和便利,然而随之而来的是新的隐私泄露问题。因此,针对位置隐私保护方法的研究受到广泛关注,位置隐私保护技术从传统的访问控制技术发展到了位置匿名技术,其中的轨迹隐私保护是研究的重点;而移动社交网络的发展带来了隐私保护新问题,已有的隐私保护方法不再适用,研究移动社交网络环境下的轨迹隐私保护技术是当前的热点。本文对轨迹隐私保护技术进行研究,针对移动社交网络中典型位置服务应用场景下的用户隐私进行保护。对于已有的位置隐私保护技术应用于移动社交网络中存在的位置数据精度不足等问题,并结合位置服务中的位置数据特点,改进目前可在数据可用性与隐私保护度取得较好平衡的泛化法和K-匿名相结合的方法,提出BMPT轨迹隐私保护方法。该方法将用户的轨迹转换为语义位置形式的轨迹序列,主要使用MPTA算法对轨迹序列进行K-匿名达到隐私保护的目的。匿名后的轨迹既满足隐私保护的需求,又满足位置服务中对于数据精度要求,有效提高了服务质量。针对轨迹匿名中用户隐私需求的差异,在BMPT轨迹隐私保护方法的基础上,提出IC-K个性化隐私保护模型。该模型首先对轨迹进行聚类处理;使用结合用户隐私需求的个性化相似度度量方法,将用户的轨迹按照不同度量标准进行聚类,使得同一类簇内的轨迹相近;对各个类簇的轨迹分别进行轨迹K-匿名,从而实现用户个性化需求的隐私保护与服务质量的较好平衡。最后通过仿真实验验证本文提出的BMPT轨迹隐私保护方法和IC-K个性化隐私保护模型的有效性及适用性。主要针对其中实现轨迹K-匿名的MPTA算法进行对比验证。通过对仿真实验结果的分析得到MPTA算法在达到轨迹K-匿名时轨迹中的位置损失较小,有较好的隐私保护效果和较高服务质量。
[Abstract]:With the rapid development of mobile Internet, mobile social network users are increasing year by year, more and more users are used to enjoy the services brought by mobile location-based applications. Traditional social networks have been closely combined with mobile Internet, forming mobile social environment with mobile, social and location services. Location-based social applications bring more pleasure and convenience to people's life and work, but with it comes a new problem of privacy disclosure. Therefore, the research on location privacy protection has been paid more and more attention. The location privacy protection technology has developed from the traditional access control technology to the location anonymity technology, and the trajectory privacy protection is the focus of the research. However, the development of mobile social network has brought about a new problem of privacy protection, and the existing privacy protection methods are no longer applicable. The research of trajectory privacy protection technology in mobile social network environment is a hot topic. In this paper, the path privacy protection technology is studied, and the user privacy is protected under the typical location service application scenario in mobile social network. For the existing location privacy protection technology used in mobile social network, the location data accuracy is insufficient, and the location data characteristics in the location service are combined. By improving the generalization method and K-anonymity method which can achieve a good balance between data availability and privacy protection, a privacy protection method for BMPT locus is proposed. In this method, the user's trajectory is transformed into a locus sequence in the form of semantic position, and the MPTA algorithm is mainly used to protect the privacy of the trajectory sequence by means of K- anonymity. The anonymous track not only meets the need of privacy protection, but also meets the requirement of data precision in location service, which effectively improves the quality of service. Aiming at the difference of user privacy requirements in locus anonymity, the IC-K personalized privacy protection model is proposed on the basis of BMPT trajectory privacy protection method. In this model, firstly, the trajectory is clustered, and the user's trajectory is clustered according to different metrics by using the personalized similarity measure method combined with the privacy requirements of the user, so that the trajectory in the same cluster is similar. The tracks of each cluster are tracked by K- anonymity, so that the privacy protection and quality of service (QoS) of users' personalized requirements can be well balanced. Finally, the effectiveness and applicability of the proposed BMPT trajectory privacy protection method and the IC-K personalized privacy protection model are verified by simulation experiments. This paper mainly compares and verifies the MPTA algorithm which implements path K-anonymity. Through the analysis of the simulation results, it is found that the MPTA algorithm has less position loss, better privacy protection and higher quality of service when it reaches the path K-anonymity.
【学位授予单位】：哈尔滨工程大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP309

【参考文献】