Flow Correlation Attacks on Tor Onion Service Sessions with Sliding Subset Sum
Bibliographic record
Abstract
Tor is one of the most popular anonymity networks in use today.Its ability to defend against flow correlation attacks is essential for providing strong anonymity guarantees.However, the feasibility of flow correlation attacks against Tor onion services (formerly known as "hidden services") has remained an open challenge.In this paper, we present an effective flow correlation attack that can deanonymize onion service sessions in the Tor network.Our attack is based on a novel distributed technique named Sliding Subset Sum (SUMo), which can be deployed by a group of colluding ISPs worldwide in a federated fashion.These ISPs collect Tor traffic at multiple vantage points in the network, and analyze it through a pipelined architecture based on machine learning classifiers and a novel similarity function based on the classic subset sum decision problem.These classifiers enable SUMo to deanonymize onion service sessions effectively and efficiently.We also analyze possible countermeasures that the Tor community can adopt to hinder the efficacy of these attacks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".