Social network de-anonymization: More adversarial knowledge, more users re-identified?

Previous works on social network de-anonymization focus on designing accurate and efficient deanonymization methods. We attempt to investigate the intrinsic relationship between the attacker's knowledge and the expected de-anonymization gain. A common intuition is that more knowledge results in more successful de-anonymization. However, our analysis shows this is not necessarily true if the attacker uses the full background knowledge for de-anonymization. Our findings leave intriguing implications for the attacker to make better use of the background knowledge for de-anonymization and for the data owners to better measure the privacy risk when releasing their data to third parties.