Personalized Privacy Protection Based on Liversity against Connection Fingerprint Attack

With the emergence and development of data mining and data publishing technology, how to protect private data and prevent sensitive information leakage has become a major challenge. According to the existing privacy protection of social networks, attackers can re-identify private users according to the connection fingerprint information of some known public users in the network, and propose two solutions based on k-anonymity clustering method. However, most anonymization methods ignore the personalized privacy protection preferences of different private users, and k-anonymity clustering model has some subtle but serious privacy problems. In this paper, the original datasets are clustered, according to the needs of different users for personalized privacy protection, to form equivalence classes model, based on the l-diversity, which improved the k-anonymity algorithm for n-range connection fingerprint attacks. At the same time, it also improves the k-anonymity algorithm for n-range fingerprint attacks, and designs the LCFC and LCNC algorithms. Finally, experiments prove that the solution proposed in this paper has very effective running time and good stability.