The Internet of Things (IoT) and Industrial 4.0 bring enormous potential benefits by enabling highly customised services and applications, which create huge volume and variety of data. However, preserving the privacy in IoT and Industrial 4.0 against re-identification attacks is very challenging. In this work, we considered three main data types generated in IoT: context data, continuous data, and media data. We first proposed a stream data anonymisation method based on k-anonymity for data collected by IoT devices; and then privacy enhancing techniques for both continuous data and media data were proposed for different IoT scenarios. The experiment results show that the proposed techniques can well preserve privacy without significantly affecting the utility of the data.

物联网（IoT）和工业4.0通过启用高度定制的服务和应用程序带来巨大的潜在利益，这些服务和应用程序会创建大量的数据和各种数据。但是，保护物联网和工业4.0的隐私以防止重新识别攻击非常具有挑战性。在这项工作中，我们考虑了物联网中生成的三种主要数据类型：上下文数据，连续数据和媒体数据。我们首先提出了一种基于k的流数据匿名化方法-物联网设备收集的数据的匿名性；然后针对不同的物联网场景提出了针对连续数据和媒体数据的隐私增强技术。实验结果表明，所提出的技术可以很好地保护隐私，而不会显着影响数据的实用性。