In spatiotemporal crowdsourcing applications, sensing data uploaded by participants usually contain spatiotemporal sensitive data. If application servers publish the unprocessed sensing data directly, it is easy to expose the privacy of participants. In addition, application servers usually adopt the static publishing mechanism, which is easy to produce problems such as poor timeliness and large information loss for spatiotemporal crowdsourcing applications. Therefore, this paper proposes a spatiotemporal privacy protection (STPP) method based on dynamic clustering methods to solve the privacy protection problem for crowd participants in spatiotemporal crowdsourcing systems. Firstly, the working principles of a dynamic privacy protection mechanism are introduced. Then, based on k-anonymity and l-diversity, the spatiotemporal sensitive data are anonymized. In addition, this paper designs the dynamic k-anonymity algorithm based on the previous anonymous results. Through extensive performance evaluation on real-world data, compared with existing methods, the proposed STPP algorithm could effectively solve the problem of poor timeliness and improve the privacy protection level while reducing the information loss of sensing data.

中文翻译：

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时空众包的动态隐私保护机制

在时空众包应用中，参与者上传的感知数据通常包含时空敏感数据。如果应用服务器直接发布未处理的传感数据，则很容易暴露参与者的隐私。另外，应用服务器通常采用静态发布机制，对于时空众包应用容易产生时效性差，信息丢失等问题。因此，本文提出了一种基于动态聚类的时空隐私保护方法，以解决时空众包系统中人群参与者的隐私保护问题。首先，介绍了动态隐私保护机制的工作原理。然后，基于k -anonymity和l-多样性，时空敏感数据被匿名化。此外，本文基于先前的匿名结果设计了动态k-匿名算法。通过对现实世界数据进行广泛的性能评估，与现有方法相比，提出的STPP算法可以有效解决时效性差的问题，提高隐私保护水平，同时减少传感数据的信息丢失。