Anonymization plays a key role in enabling the public release of network datasets, yet there are few, if any, techniques for evaluating the efficacy of network data anonymization techniques with respect to the privacy they afford. Specifically, we simulate the behavior of an adversary whose goal is to deanonymize objects, such as hosts or web pages, within the network data. By doing so, we are able to quantify the anonymity of the data using information theoretic metrics, objectively compare the efficacy of anonymization techniques, and examine the impact of selective deanonymization on the anonymity of the data.