With the ever-increasing number of devices, the Internet of Things facilitates the connection between the devices in the hyper-connected world. As the number of interconnected devices increases, sensitive data disclosure becomes an important issue that needs to be addressed. In order to prevent the disclosure of sensitive data, effective and feasible privacy preservation strategies are necessary. A noise-based privacy-preserving model has been proposed in this article. The components of the noise-based privacy-preserving model include Multilevel Noise Treatment for data collection; user preferences-based data classifier to classify sensitive and non-sensitive data; Noise Removal and Fuzzification Mechanism for data access and user-customized privacy preservation mechanism. Experiments have been conducted to evaluate the performance and feasibility of the proposed model. The results have been compared with existing approaches. The experimental results show an improvement in the proposed noise-based privacy-preserving model in terms of computational overhead. The comparative analysis indicates that the proposed model without the fuzzifier has around 52–77% less computational overhead than the Data access control scheme and 46–70% less computational overhead compared to the Dynamic Privacy Protection model. The proposed model with the fuzzifier has around 48–73% less computational overhead compared to the Data access control scheme and 31–63% less computational overhead compared to the Dynamic Privacy Protection model. Furthermore, the privacy analysis has been done with the relevant approaches. The results indicate that the proposed model can customize privacy as per the users’ preferences and at the same time takes less execution time which reduces the overhead on the resource constraint IoT devices.

随着设备数量的不断增加，物联网促进了超连接世界中设备之间的连接。随着互连设备数量的增加，敏感数据泄露成为需要解决的重要问题。为了防止敏感数据的泄露，需要有效可行的隐私保护策略。本文提出了一种基于噪声的隐私保护模型。基于噪声的隐私保护模型的组成部分包括用于数据收集的多级噪声处理；基于用户偏好的数据分类器对敏感和非敏感数据进行分类；数据访问的降噪和模糊化机制和用户定制的隐私保护机制。已经进行了实验以评估所提出模型的性能和可行性。结果已与现有方法进行了比较。实验结果表明，所提出的基于噪声的隐私保护模型在计算开销方面有所改进。比较分析表明，与数据访问控制方案相比，所提出的没有模糊器的模型的计算开销减少了约 52-77%，与动态隐私保护模型相比，计算开销减少了 46-70%。与数据访问控制方案相比，所提出的带有模糊器的模型的计算开销减少了约 48-73%，与动态隐私保护模型相比，计算开销减少了 31-63%。此外，还使用相关方法进行了隐私分析。