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AbstractÐPrivacy of data as well as providing anonymization of data for various kinds of analysis have been addressed in the context of tabular transactional data which was mainstream. With the advent of the Internet and social networks, there is an emphasis on using different kinds of graphs for modeling and analysis. In addition to single graphs, the use of MultiLayer Networks (or MLNs) for modeling and analysis is becoming popular for complex data having multiple types of entities and relationships. They provide a better understanding of data as well as flexibility and efficiency of analysis. In this article, we understand the provenance of data privacy and some of the thinking on extending it to graph data models. We will focus on the issues of data privacy for models that are different from traditional data models and discuss alternatives. We will also consider privacy from a visualization perspective as we have developed a community Dashboard for MLN generation, analysis, and visualization based on our research.