中文摘要：

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。

英文摘要：

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 . T hen 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 .