Novel Approach to Tor User De-Anonymization: Client-Side Generated Watermark

Most of the existing techniques for Tor user de-anonymization by means of traffic-watermarks are generally implemented through utilization/modulation of server-side originating traffic. Unfortunately, the real-world effectiveness of server-side originating watermarks is often hindered by significant amounts of traffic noise that accumulates along Tor's communication pathways. In this paper, we outline the key ideas behind our novel user de-anonymization technique that utilizes client-side originating watermarks. We describe some potential ways this scheme could be implemented in practice while not requiring the control of any Tor node or other resource. We also demonstrate significantly superior real-world performance of our approach vs those previously discussed in the literature.

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Publication Details

Published
2024-09-02
DOI
https://doi.org/10.1109/csr61664.2024.10679508
Primary Topic
Internet Traffic Analysis and Secure E-voting
Type
article
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article

Novel Approach to Tor User De-Anonymization: Client-Side Generated Watermark

Daniel R. L. Brown, Natalija Vlajic
Internet Traffic Analysis and Secure E-voting
article

Novel Approach to Tor User De-Anonymization: Client-Side Generated Watermark

Daniel R. L. Brown, Natalija Vlajic
article en

Abstract

Most of the existing techniques for Tor user de-anonymization by means of traffic-watermarks are generally implemented through utilization/modulation of server-side originating traffic. Unfortunately, the real-world effectiveness of server-side originating watermarks is often hindered by significant amounts of traffic noise that accumulates along Tor's communication pathways. In this paper, we outline the key ideas behind our novel user de-anonymization technique that utilizes client-side originating watermarks. We describe some potential ways this scheme could be implemented in practice while not requiring the control of any Tor node or other resource. We also demonstrate significantly superior real-world performance of our approach vs those previously discussed in the literature.

York University (CA)
Openalex Percentile: Top 86%
Internet Traffic Analysis and Secure E-voting
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