Computer Networks: The International Journal of Computer and Telecommunications Networking
Profiling internet backbone traffic: behavior models and applications
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
The devil and packet trace anonymization
ACM SIGCOMM Computer Communication Review
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
A first look at modern enterprise traffic
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
FLAIM: a multi-level anonymization framework for computer and network logs
LISA '06 Proceedings of the 20th conference on Large Installation System Administration
Sanitization models and their limitations
NSPW '06 Proceedings of the 2006 workshop on New security paradigms
Exploiting the IPID field to infer network path and end-system characteristics
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
On the privacy risks of publishing anonymized IP network traces
CMS'06 Proceedings of the 10th IFIP TC-6 TC-11 international conference on Communications and Multimedia Security
PET'05 Proceedings of the 5th international conference on Privacy Enhancing Technologies
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Network security research can benefit greatly from testing environments that are capable of generating realistic, repeatable and configurable background traffic. In order to conduct network security experiments, researchers require isolated testbeds capable of recreating actual network environments, complete with infrastructure and traffic details. Unfortunately, due to privacy and flexibility concerns, actual network traffic is rarely shared by organizations. Trace data anonymization is one solution to this problem. The research community has responded to this sanitization problem with anonymization tools that aim to remove sensitive information from network traces, and attacks on anonymized traces that aim to evaluate the efficacy of the anonymization schemes. However there is continued lack of a comprehensive model that distills all elements of the sanitization problem into a functional reference model. In this paper we offer such a comprehensive functional reference model that identifies and binds together all the entities required to formulate the problem of network data anonymization. We also build a new information flow model that illustrates the overly optimistic nature of inference attacks on anonymized traces. We also provide a probabilistic interpretation of the information model and develop a privacy metric for anonymized traces.