| Abstract: |
Network operators are reluctant to share traffic data due to security and privacy concerns. Consequently, there is a lack of publicly available traces for validating and generalizing the
latest results in network and security research. Anonymization is a possible solution in this context; however, it is unclear how the sanitization of data preserves characteristics important for
traffic analysis. In addition, the privacy-preserving property of state-of-the-art IP address anonymization techniques has come into question by recent attacks that successfully identified a
large number of hosts in anonymized traces. In this paper, we examine the tradeoff between data utility for anomaly detection and the risk of host identification for IP address truncation.
Specifically, we analyze three weeks of unsampled and non-anonymized network traces from a medium-sized backbone network to assess data utility. The risk of de-anonymizing individual IP addresses
is formally evaluated, using a metric based on conditional entropy. Our results indicate that truncation effectively prevents host identification but degrades the utility of data for anomaly
detection. However, the degree of degradation depends on the metric used and whether network-internal or external addresses are considered. Entropy metrics are more resistant to truncation than
unique counts and the detection quality of anomalies degrades much faster in internal addresses than in external addresses. In particular, the usefulness of internal address counts is lost even
for truncation of only 4 bits whereas utility of external address entropy is virtually unchanged even for truncation of 20 bits. |
