Effects of External Information on Anonymity and Role of Transparency with Example of Social Network De-anonymisation

Personal data to be used for sophisticated data-centric services is typically anonym zed to ensure the privacy of the underlying individuals. However, the degree of protection against de-anonymization is uncertain because de-anonymization has not been explicitly modelled for scientific analysis and because the information that attackers might use is not well defined given that they can use a wide variety of external information sources such as public ally accessible information on the web. We have developed a system that de-anonymizes anonymous posts on social networks with a considerably high precision rate. We analysed this system and its behaviour and, on the basis of our findings, we clarified a de-anonymization model and used it to clarify the effects of external information on anonymity. We also identified the limitation of anonymization under conditions of external information being available and clarified the role of transparency can play in controlling the use of personal data.