Investigating Data Flows from Android Games

Online applications can share data with arbitrary parties, without users noticing. In practice, these data flows could constitute privacy violations to individual users, with the lack of transparency leaving users unaware of the unwanted data flows. To understand the spread and scale of this privacy threat, we investigate what data is collected and where it is sent in popular free-to-play Android games. As part of our investigation, we conduct a measurement study where we collect and analyze over 16 GB of network traffic from 50 puzzle games. Additionally, we explore the effectiveness of user-side countermeasures for a selection of the apps. We find that while most countermeasures, such as ad-blocking DNS services, decrease data flows, withholding consent and opting out of data collection can produce inconsistent behavior including increased outgoing traffic. To encourage further analysis, we release our measurements as an open dataset.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-10-29
DOI
https://doi.org/10.48550/arxiv.2610.39730
Primary Topic
Privacy, Security, and Data Protection
Type
preprint
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preprint

Investigating Data Flows from Android Games

Boel Nelson, Lukas Moog, Judit Borcsiczky
arXiv (Cornell University)
Privacy, Security, and Data Protection
preprint

Investigating Data Flows from Android Games

Boel Nelson, Lukas Moog, Judit Borcsiczky
preprint en

Abstract

Online applications can share data with arbitrary parties, without users noticing. In practice, these data flows could constitute privacy violations to individual users, with the lack of transparency leaving users unaware of the unwanted data flows. To understand the spread and scale of this privacy threat, we investigate what data is collected and where it is sent in popular free-to-play Android games. As part of our investigation, we conduct a measurement study where we collect and analyze over 16 GB of network traffic from 50 puzzle games. Additionally, we explore the effectiveness of user-side countermeasures for a selection of the apps. We find that while most countermeasures, such as ad-blocking DNS services, decrease data flows, withholding consent and opting out of data collection can produce inconsistent behavior including increased outgoing traffic. To encourage further analysis, we release our measurements as an open dataset.

arXiv (Cornell University)
Privacy, Security, and Data Protection
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