# A Privacy Measurement Framework for Multiple Online Social Networks against Social Identity Linkage
## 2 Related Work  
The role of OSNs represents social relationships that exist in real life, which is called the real-life social network (RSN). The PII stored in OSNs can be modelled as an online social graph, and there is a one-to-one mapping from the RSN to the online social graph model. If multiple OSNs are used, this mapping can be found especially quickly, accurately, and inexpensively due to SIL research. Therefore, the disclosure of PII may lead to malicious attacks from the cyberspace and real world [24; 25]; examples of these attacks include, but are not limited to, tracking, defamation, spamming, phishing, identity theft, profile cloning, Sybil attack, etc. In recent years, many studies have been performed on privacy preservation via data mining and publishing; additionally, some privacy protection methods have been proposed for specific scenarios, such as RFID(Radio Frequency Identification) and smart grids [26; 27], but not much has been explored related to user privacy awareness, which can be defined as the individual's awareness of the actionsand behaviours required to protect their personal information [28; 29]. In contrast, SIL is developing rapidly [12; 13; 14; 15; 16; 17; 18; 19; 20]. In the existing research on privacy metrics, some researchers measure a single aspect. For example, [PERSON] et al. [30] studied the amount of work that has been done regarding the harm to users caused by the disclosure of age information. [PERSON] et al. [31] conducted an in-depth study on the privacy disclosure of a social network from the perspective of image deletion delay. [PERSON] et al. [32] discussed the issue of privacy disclosure from the dissemination of character information. Meanwhile, others have tried to solve the problem based on the overall consideration of the user. [PERSON] et al. [33] discussed the concepts of attribute sensitivity and profile visibility, then evaluated these two values using a Bayesian method to evaluate privacy. [PERSON] et al. [34] broadened their study in a different way: Using item response theory (IRT) in combination with sensitivity and visibility to provide an intuitive and mathematical approach for calculating privacy metrics of OSNs. [PERSON] et al. [7] devised a template for privacy wizards to help users complete profile settings; however, they did not explain why. In the opinion of [PERSON] et al. [35], personal privacy disclosure levels are based on the protection of information that public friends disclose; they proposed a framework to assess the privacy disclosure in a community. Similarly, [PERSON] et al. [36] believed that attackers could use the background knowledge of public neighbours to obtain the privacy of victims; they used K-anonymity and L-diversity to approach this challenge, but these two approaches do not completely solve the issue. [PERSON] et al. [23] used a circle-based definition of privacy score to measure privacy leakage and applied a learning approach to help users change privacy settings. Currently, users are not confined to using only one social network. Thus, the above studies, which are based on a single platform, cannot accurately quantify privacy leakage. However, studies based on multiple OSNs are extremely rare and immature. [PERSON] et al. [37] revealed that attackers can aggregate a user's PII on multiple OSNs for identity and password recovery attacks. However, they did not propose an effective method for solving this problem, only suggesting that users should disclose their PII as little as possible. [PERSON] et al. [25] proposed a framework based on scenarios with multiple social networks that can achieve the goal of protecting user privacy; however, it is impossible for each social network to interact with each other in practice. [PERSON] et al. [38] combined attribute sensitivity and visibility, and used statistical fuzzy systems to solve the problem of privacy metrics on multiple OSNs.