Playing Devil's Advocate: Inferring Sensitive Information from Anonymized Network Traces.

TLDR

Releasing network data supports research but can expose sensitive information about the publishing organization. The study introduces techniques that infer network topology and deanonymize servers from anonymized data alone, and discusses ways to prevent such attacks. The methods achieve this by leveraging only the anonymized traces and publicly available information. Analyses on three datasets demonstrate that the techniques can uncover substantial sensitive information.

Abstract

Encouraging the release of network data is central to promoting sound network research practices, though the publication of this data can leak sensitive information about the publishing organization. To address this dilemma, several techniques have been suggested for anonymizing network data by obfuscating sensitive fields. In this paper, we present new techniques for inferring network topology and deanonymizing servers present in anonymized network data, using only the data itself and public information. Via analyses on three different network datasets, we quantify the effectiveness of our techniques, showing that they can uncover significant amounts of sensitive information. We also discuss prospects for preventing these deanonymization attacks.