AASC: Anonymizing network addresses based on subnet clustering

The network packet trace dataset plays an important role in networking research. Publishing those traces publicly faces how to protect the providers´ sensitive privacy, especially the internal IP addresses. In this paper, we propose a subnet-clustering based method, AASC, to anonymize those internal addresses. According to AASC, three parts of a whole IP address are anonymized by different methods. The network part is anonymized by a prefix-preserved anonymization method, the subnet part is generalized by clustering based on a predefined set of port numbers, and the host address is randomized. We also define two entropy based metrics, the simple measure and the co-existence measure, to measure the degree of privacy preserved in anonymized addresses. The defined metrics can reflect some dependencies among trace records. We develop a local-search based, measure-guided algorithm to search subnet clusters with more utilities. We have conducted some experiments to validate our proposed method.