Approximating a Global Passive Adversary Against Tor
- Approximating a Global Passive Adversary Against Tor
- Chakravarty, Sambuddho
Keromytis, Angelos D.
- Computer Science
- Persistent URL:
- Columbia University Computer Science Technical Reports
- Part Number:
- Department of Computer Science, Columbia University
- Publisher Location:
- New York
- We present a novel, practical, and effective mechanism for identifying the IP address of Tor clients. We approximate an almost-global passive adversary (GPA) capable of eavesdropping anywhere in the network by using LinkWidth, a novel bandwidth-estimation technique. LinkWidth allows network edge-attached entities to estimate the available bandwidth in an arbitrary Internet link without a cooperating peer host, router, or ISP. By modulating the bandwidth of an anonymous connection (e.g., when the destination server or its router is under our control), we can observe these fluctuations as they propagate through the Tor network and the Internet to the end-user's IP address. Our technique exploits one of the design criteria for Tor (trading off GPA-resistance for improved latency/bandwidth over MIXes) by allowing well-provisioned (in terms of bandwidth) adversaries to effectively become GPAs. Although timing-based attacks have been demonstrated against non-timing-preserving anonymity networks, they have depended either on a global passive adversary or on the compromise of a substantial number of Tor nodes. Our technique does not require compromise of any Tor nodes or collaboration of the end-server (for some scenarios). We demonstrate the effectiveness of our approach in tracking the IP address of Tor users in a series of experiments. Even for an underprovisioned adversary with only two network vantage points, we can identify the end user (IP address) in many cases.
- Computer science
Computer networks--Security measures
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- Suggested Citation:
- Sambuddho Chakravarty, Angelos Stavrou, Angelos D. Keromytis, 2008, Approximating a Global Passive Adversary Against Tor, Columbia University Academic Commons, https://doi.org/10.7916/D82J6QBM.