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ARBA:基于分解重构技术的LBS隐私保护方法

A novel anonymization method based on anatomy and reconstruction in LBS privacy preservation

  • 摘要: 现有的匿名化方法多采用时空伪装技术,该技术计算负担重,LBS响应延迟时间长,导致LBS服务质量低.为此,提出了分解重构的匿名化方法,该方法首先对接收到的LBS查询集进行分组,形成满足匿名模型的等价类,然后对每个等价类根据不同的策略进行分解和重构,生成新的匿名查询集.此外,面向多种隐私需求,提出了一系列匿名模型,并进一步提出了基于分解重构技术的匿名模型的实现算法MBFAA.实验表明,提出的重构分解技术可以有效地实现各种匿名模型.

     

    Abstract: Most of the existing methods are realized by temporal and spatial cloaking techniques. However, these cloaking-based methods are disadvantageous due to their high computation loads and long response delays, which lowers service quality. To address these problems, a novel technique, anatomy and reconstruction, was proposed. This technique first partitions the LBS query set into several equivalence classes, making sure that each equivalence class satisfies the given anonymity constraints. Then it reconstructs the LBS queries in each equivalence class according to the predefined strategies separately, and generates a new set of anonymous queries. Considering various privacy requirements, a series of anonymity models were proposed, and a unified anonymization algorithm MBFAA was introduced to realize these models. Experimental results show that the proposed method can effectively implement all the anonymity models.

     

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