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The growing popularity of social networks and the increasing need for publishing related data mean that protection of privacy becomes an important and challenging problem in social networks. This paper describes the (k,l k,l k,l)-anonymity model used for social network graph anonymization. The method is based on edge addition and is utility-aware, i.e. it is designed to generate a graph that is similar to the original one. Different strategies are evaluated to this end and the results are compared based on common utility metrics. The outputs confirm that the na¨ıve idea of adding some random or even minimum number of possible edges does not always produce useful anonymized social network graphs, thus creating some interesting alternatives for graph anonymization techniques.

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