Due to the pervasiveness of always connected devices, large amounts of heterogeneous data are continuously being collected. Beyond the benefits that accrue for the users, there are private and sensitive information that is exposed. Therefore, Privacy-Preserving Mechanisms (PPMs) are crucial to protect users’ privacy. In this paper, we perform a thorough study of the state of the art on the following topics: heterogeneous data types, PPMs, and tools for privacy protection. Building from the achieved knowledge, we propose a privacy taxonomy that establishes a relation between different types of data and suitable PPMs for the characteristics of those data types. Moreover, we perform a systematic analysis of solutions for privacy protection, by presenting and comparing privacy tools. From the performed analysis, we identify open challenges and future directions, namely, in the development of novel PPMs.

由于始终连接的设备无处不在，因此不断收集大量的异构数据。除了为用户带来的好处外，还公开了私人和敏感信息。因此，隐私保护机制（PPM）对于保护用户的隐私至关重要。在本文中，我们对以下主题进行了最全面的研究：异构数据类型，PPM和隐私保护工具。基于所获得的知识，我们提出了一种隐私分类法，该分类法在不同类型的数据和适用于这些数据类型特征的适当PPM之间建立了联系。此外，我们通过展示和比较隐私工具来对隐私保护解决方案进行系统的分析。根据执行的分析，