Enhancement of k-anonymity algorithm for privacy preservation in social media

In recent times, more and more social data is transmitted in different ways. Protecting the privacy of social network data has turn out to be an essential issue. Hypothetically, it is assumed that the attacker utilizes the similar information used by the genuine user. With the knowledge obtained from the users of social networks, attackers can easily attack the privacy of several victims. Thus, assuming the attacks or noise node with the similar environment information does not resemble the personalized privacy necessities, meanwhile, it loses the possibility to attain better utility by taking benefit of differences of users’ privacy necessities. The traditional research on privacy-protected data publishing can only deal with relational data and even cannot applied to the data of social networking. In this research work, K-anonymity is used for providing the security of the sensitive information from the attacker in the social network. K-anonymity provides security from attacker by making the graph and developing nodes degree. The clusters are made by grouping the similar degree into one group and the process is repeated until the noisy node is identified. For measuring the efficiency the parameters named as Average Path Length (APL) and information loss are measured. A reduction of 0.43% of the information loss is obtained.