Tématerületi Kiválósági Program 2019(ED_18-1-2019-0030) Támogató: NKFIH
Due to the needs of social analysis, behavior prediction and personal customization,
social network data is collected and released in large quantities. In this process,
the abuse of private information is a widespread problem. The contradiction between
the public service of social networks and the protection of individual private information
has become one of the focus issues of current privacy protection. There are mainly
three quizzes in cloud computing environment: large data privacy protection, data
credibility and access control. K-anonymization [1.] as a main data anonymity protection
technology, shows its advantages in neighbour attacks and privacy-preserving data
publishing (PPDP). However, most of the researches focus on static, one-time release.
Thus, these models can not prevent data analysis by collision attacks, and data anonymization
in cloud computing environment. In overview, it is a survey paper, and we compare
and analyze the differences of various approaches of social network anonymization.