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A Particle Swarm Optimization Clustering-Based Attribute Generalization Privacy Protection Scheme

机译：基于粒子群优化聚类的属性通用隐私保护方案

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摘要

Continuous query in location-based services may reveal the attribute information of the user obliviously, and an adversary may utilize the attribute as background knowledge to correlate the real locations and to generate location trajectory. Thus, the adversary can obtain the personal privacy of the user. In order to cope with this problem, several algorithms had been proposed. However, these algorithms were mainly designed for snapshot query and failed to provide privacy protection service for continuous query. As a matter of fact, continuous anonymous regions can also be used as the trajectory of regions and one can obtain the real location trajectory through calibration. In addition, other algorithms designed for continuous query may also utilize a longer running time to achieve the attribute anonymity and affect the balance of quality of service and personal privacy. Therefore, in order to cope with the above two problems, this paper provides a PSO anonymization, short for particle swarm optimization anonymization algorithm. This algorithm utilizes the particle swarm optimization clustering algorithm to accelerate the process of finding similar attributes in attribute generalization. Furthermore, this algorithm also utilizes the randomly chosen anonymous cells to further generalize the anonymous region, so that it can provide better privacy protection and better service quality. At last, this paper utilizes security analysis and experimental verification to further verify the effectiveness and efficiency of both the level of privacy protection and algorithm execution.
机译：在基于位置的服务中的连续查询可以明显地揭示用户的属性信息，并且对手可以利用该属性作为背景知识来关联真实位置并生成位置轨迹。因此，对手可以获得用户的个人隐私。为了解决这个问题，已经提出了几种算法。但是，这些算法主要是为快照查询设计的，无法为连续查询提供隐私保护服务。实际上，连续的匿名区域也可以用作区域的轨迹，并且可以通过校准获得真实的位置轨迹。另外，为连续查询而设计的其他算法也可能利用更长的运行时间来实现属性匿名，并影响服务质量和个人隐私的平衡。因此，为解决上述两个问题，本文提出了一种粒子群优化匿名化的PSO匿名化算法。该算法利用粒子群优化聚类算法加快了属性归纳中寻找相似属性的过程。此外，该算法还利用随机选择的匿名小区来进一步泛化匿名区域，从而可以提供更好的隐私保护和更好的服务质量。最后，本文通过安全性分析和实验验证，进一步验证了隐私保护和算法执行水平的有效性和效率。

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