Attribute Inference Attacks in Online Multiplayer Video Games: A Case Study on DOTA2
Bibliographic record
Abstract
Did you know that over 70 million of Dota2 players have their ingame data freely accessible?What if such data is used in malicious ways?This paper is the first to investigate such a problem.Motivated by the widespread popularity of video games, we propose the first threat model for Attribute Inference Attacks (AIA) in the Dota2 context.We explain how (and why) attackers can exploit the abundant public data in the Dota2 ecosystem to infer private information about its players.Due to lack of concrete evidence on the efficacy of our AIA, we empirically prove and assess their impact in reality.By conducting an extensive survey on ∼500 Dota2 players spanning over 26k matches, we verify whether a correlation exists between a player's Dota2 activity and their real-life.Then, after finding such a link (𝑝 < 0.01 and 𝜌 > 0.3), we ethically perform diverse AIA.We leverage the capabilities of machine learning to infer real-life attributes of the respondents of our survey by using their publicly available in-game data.Our results show that, by applying domain expertise, some AIA can reach up to 98% precision and over 90% accuracy.This paper hence raises the alarm on a subtle, but concrete threat that can potentially affect the entire competitive gaming landscape.We alerted the developers of Dota2.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".