Multidomain Fusion Data Privacy Security Framework

Abstract

With the collaborative collection of the Internet of Things (IoT) in multidomain, the collected data contains richer background knowledge. However, this puts forward new requirements for the security of data publishing. Furthermore, traditional statistical methods ignore the attributes sensitivity and the relationship between attributes, which makes multimodal statistics among attributes in multidomain fusion data set based on sensitivity difficult. To solve the above problems, this paper proposes a multidomain fusion data privacy security framework. First, based on attributes recognition, classification, and grading model, determine the attributes sensitivity and relationship between attributes to realize the multimode data statistics. Second, combine them with the different modal histograms to build multimodal histograms. Finally, we propose a privacy protection model to ensure the security of data publishing. The experimental analysis shows that the framework can not only build multimodal histograms of different microdomain attribute sets but also effectively reduce frequency query error.