Finding "Who Is Talking to Whom" in VoIP Networks via Progressive Stream Clustering
Technologies that use the Internet network to deliver voice communications have the potential to reduce costs and improve access to communications services around the world. However, these new technologies pose several challenges in terms of confidentiality of the conversations and anonymity of the conversing parties. Call authentication and encryption techniques provide a way to protect confidentiality, while anonymity is typically preserved by an anonymizing service (anonymous call). This work studies the feasibility of revealing pairs of anonymous and encrypted conversing parties (caller/callee pair of streams) by exploiting the vulnerabilities inherent to VoIP systems. In particular, by exploiting the aperiodic inter-departure time of VoIP packets, we can trivialize each VoIP stream into a binary time-series. We first define a simple yet intuitive metric to gauge the correlation between two VoIP binary streams. Then we propose an effective technique that progressively pairs conversing parties with high accuracy and in a limited amount of time. Our metric and method are justified analytically and validated by experiments on a very large standard corpus of conversational speech. We obtain impressively high pairing accuracy that reaches 97% after 5 minutes of voice conversations.