  In recent years there has been a significant increase in the use of graphs as
a tool for representing information. It is very important to preserve the
privacy of users when one wants to publish this information, especially in the
case of social graphs. In this case, it is essential to implement an
anonymization process in the data in order to preserve users' privacy. In this
paper we present an algorithm for graph anonymization, called Evolutionary
Algorithm for Graph Anonymization (EAGA), based on edge modifications to
preserve the k-anonymity model.
