**EAGA (Evolutionary Algorithm for Graph Anonymization) Algorithm**

EAGA is an algorithm for k-degree anonymization on networks. It uses the concept of evolutionary algorithm to anonymize the degree sequence of the network. Then, it applies edge swap to modify graph structure in order to implement to the anonymized k-degree sequence.

It was presented in:

*Casas-roma, J., Herrera-Joancomartí, J., & Torra, V. (2013). Evolutionary Algorithm for Graph Anonymization. Retrieved from http://arxiv.org/abs/1310.0229v2*

Abstract: 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 Algorithms for Graph Anonymization (EAGA), based on edge modifications to preserve the k-anonymity model.

The code (implemented in Java) can be downloaded:

For any trouble or comment, please feel free to contact me.

Can you give me example dataset to which this code works.

This algorithm only works with small networks (less than 1,000 nodes).

It works, for instance, on Zachary’s Karate Club (a very well-known network in community detection algorithms) using GML format. You can download it here:

http://www-personal.umich.edu/~mejn/netdata/

I suggest you to use UMGA, which also based on k-degree anonymity and it is able to anonymize large networks with thousands or millions of vertices and edges. Source code is also available here.

Regards,