Abstract

AbstractNetworking researchers and engineers rely on network packet traces for understanding network behavior, developing models, and evaluating network performance. Although the bulk of published packet traces implement a form of address anonymization to hide sensitive information, it has been unclear if such anonymization techniques are sufficient to address the privacy concerns of users and organizations.In this paper we attempt to quantify the risks of publishing anonymized packet traces. In particular, we examine whether statistical identification techniques can be used to uncover the identities of users and their surfing activities from anonymized packet traces. Our results show that such techniques can be used by any Web server that is itself present in the packet trace and has sufficient resources to map out and keep track of the content of popular Web sites to obtain information on the network-wide browsing behavior of its clients. Furthermore, we discuss how scan sequences identified in the trace can easily reveal the mapping from anonymized to real IP addresses.KeywordsPrivacy RiskPage RequestPacket TracePairwise MatchLocal SubnetThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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