Attribute Inference Attacks in Online Multiplayer Video Games

Attribute Inference Attacks in Online Multiplayer Video Games: A Case Study on DOTA2

Tricomi, Pier Paolo (Università degli Studi di Padova)
Facciolo, Lisa (Università degli Studi di Padova)
Apruzzese, Giovanni (Universität Liechtenstein)
Conti, M. (TU Delft Cyber Security)

2023

Did you know that over 70 million of Dota2 players have their in-game 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 applyingdomain 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.

attribute inference attack
dota2
machine learning
video games

http://resolver.tudelft.nl/uuid:d934d08a-d67b-44c6-a6f5-c4a140e06a37

10.1145/3577923.3583653

Association for Computing Machinery (ACM)

2023-12-04

9798400700675

CODASPY 2023 - Proceedings of the 13th ACM Conference on Data and Application Security and Privacy

13th ACM Conference on Data and Application Security and Privacy, CODASPY 2023, 2023-04-24 → 2023-04-26, Charlotte, United States

CODASPY 2023 - Proceedings of the 13th ACM Conference on Data and Application Security and Privacy

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2023 Pier Paolo Tricomi, Lisa Facciolo, Giovanni Apruzzese, M. Conti