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Top1. Introduction
In recent years, hack attack, virus, trojan and personal information disclosure are increasingly exposed to public view, network security has attracted much attention. The existing network security technologies have a broad definition of protected data, which does not divide the types of data. Most of the time, they treat the protected data equally. Network service providers don’t think differently about different types of data when they grab network data, also they don’t communicate with data providers(Vincent et al., 2011). In fact, different types of data need different degrees of protection, especially personal privacy data. For example, someone's gender is one privacy data that can be open to a lot of people, such as friends, colleagues and so on, passwprd of bank card is also kind of privacy data, but it is confidentiality to others, which can only be kept by himself.
Many network activities may cause personal privacy information disclosure, such as website registration, online shopping, clicking links and so on. What information is disclosed in process of network activity is difficult to capture, even if it can be captured, it is difficult to measure what impact it will have on the data owner after privacy information is disclosed. However, there is a certain connection between different types of personal privacy data(Omoronyia, 2016), more data can be derived from one or more exist data. However, these connections are not considered in network activities. Most existing privacy protection schemes can only match individual privacy information. The P3P platform(Cranor et al., 2002) which proposed in 2007 provides a method for personalized privacy protection, enabling users to define their privacy preferences. But P3P platform has not done any processing for the relationship between different privacy information, also has not used specific numbers to quantify these links.
Ontology is a good tool to find the relationship between different data. In an ontology library, the correlation between two separate data can be calculated. Because of the existence of reasoning relationship, most of the existing privacy protection methods will actually still cause privacy disclosure. In order to understand the situation of privacy disclosure in real world, this paper conducts a privacy disclosure measurement experiment on ten public figures in different fields, so as to understand what kind of privacy information is easily disclosed by people in different fields. By calculating of a privacy measurement algorithm based on WordNet ontology, the degree of disclosure of these public figures' privacy information is quantified with a specific number and analyzed comparatively.
The rest of this paper is organized as follows. Section 2 review related work include privacy protection, ontology based privacy protection and privacy measurement. Section 3 introduces the relationship and structure of WordNet. Section 4 builds a privacy disclosure model and defines the goal. Section 5 introduces the quantification algorithm, include the framework of algorithm, the relationship handling and the characteristics of algorithm. Section 6 is the experimental part and section 7 is the conclusion.