A large-scale dataset of Android applications and their SDK dependencies

• URL: http://arxiv.org/abs/2607.02627v1
• Date: Thu, 02 Jul 2026 13:19:42 GMT
• Title: A large-scale dataset of Android applications and their SDK dependencies
• Abstract summary: Mobile apps increasingly rely on third-party Software Development Kits (SDKs) to provide services such as advertising, analytics, authentication, crash reporting, and location services.<n>Here, we present a large-scale dataset linking Android apps to the third-party SDKs they integrate.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile applications (apps) increasingly rely on third-party Software Development Kits (SDKs) to provide services such as advertising, analytics, authentication, crash reporting, and location services. These components form an important, but often hidden, layer of the mobile ecosystem. Here, we present a large-scale dataset linking Android apps to the third-party SDKs they integrate. The dataset was constructed by combining app package files (APKs) and metadata from AndroZoo with SDK detection rules provided by Exodus Privacy. We implemented a reproducible, static-analysis pipeline that downloads APKs, inspects their compiled code, and detects SDKs through code-level signature matching: the released dataset contains 334,719 unique app-version observations (associated with 99,722 Android apps) and 246 third-party SDKs - including app-level metadata such as categories, comments, file size, download counts, ratings and SDK-level metadata such as categories, code roots, code versions, names. The dataset supports the construction of a large-scale app-SDK dependency network and its projection onto both layers; in addition, SDKs are mapped to their corresponding operating companies, enabling provider-level analysis of technological concentration and upstream control in the mobile ecosystem. To support reproducible research, the pipeline and dataset are publicly released on GitHub and Zenodo; more broadly, the project provides a reusable research infrastructure for studying third-party technological dependencies, data-collection capabilities, and privacy infrastructures across Android apps.

Related papers

• Ecosystem-Driven Privacy Exposure in Mobile Gaming Apps: A Configuration-Aware Empirical Analysis [0.40611352512781873]
  This study presents a configuration aware empirical assessment of privacy exposure in Android mobile gaming apps.<n>A systematic static analysis was conducted on 41 widely deployed Android mobile gaming apps collected from the Google Play ecosystem.
  arXiv  Detail & Related papers  (2026-05-25T11:14:04Z)
• OpenMobile: Building Open Mobile Agents with Task and Trajectory Synthesis [98.43366988856592]
  We present OpenMobile, an open-source framework that synthesizes high-quality task instructions and agent trajectories.<n>Agents trained on our data achieve competitive results across three dynamic mobile agent benchmarks.
  arXiv  Detail & Related papers  (2026-04-16T14:53:08Z)
• DeepXiv-SDK: An Agentic Data Interface for Scientific Literature [60.19264121557117]
  DeepXiv-SDK is a three-layer agentic data interface for scientific literature.<n>DeepXiv-SDK supports the complete ArXiv corpus, and is synchronized daily to incorporate new releases.
  arXiv  Detail & Related papers  (2026-02-14T23:07:28Z)
• Fingerprinting SDKs for Mobile Apps and Where to Find Them: Understanding the Market for Device Fingerprinting [12.531894763016172]
  This paper presents a large-scale analysis of fingerprinting-like behavior in the mobile application ecosystem.<n>We take a market-based approach, focusing on third-party tracking as enabled by applications' common use of third-party SDKs.<n>To the best of our knowledge, this represents the largest-scale analysis of SDK behavior undertaken to date.
  arXiv  Detail & Related papers  (2025-06-27T21:05:48Z)
• Scalable Video-to-Dataset Generation for Cross-Platform Mobile Agents [57.59830804627066]
  We introduce MONDAY, a large-scale dataset of 313K annotated frames from 20K instructional videos capturing real-world mobile OS navigation.<n>Models that include MONDAY in their pre-training phases demonstrate robust cross-platform generalization capabilities.<n>We present an automated framework that leverages publicly available video content to create comprehensive task datasets.
  arXiv  Detail & Related papers  (2025-05-19T02:39:03Z)
• Your Signal, Their Data: An Empirical Privacy Analysis of Wireless-scanning SDKs in Android [3.830984415546144]
  Mobile apps frequently use Bluetooth Low Energy (BLE) and WiFi scanning permissions to discover nearby devices.<n>Wireless interfaces also serve as a covert proxy for geolocation data, enabling continuous user tracking and profiling.<n>We conduct the first systematic analysis of 52 wireless-scanning SDKs, revealing their data collection practices and privacy risks.
  arXiv  Detail & Related papers  (2025-03-19T14:15:02Z)
• Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting [64.7364925689825]
  Argoverse 2 (AV2) is a collection of three datasets for perception and forecasting research in the self-driving domain. The Lidar dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. The Motion Forecasting dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene.
  arXiv  Detail & Related papers  (2023-01-02T00:36:22Z)
• Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans [103.92680099373567]
  This paper introduces a pipeline to parametrically sample and render multi-task vision datasets from comprehensive 3D scans from the real world. Changing the sampling parameters allows one to "steer" the generated datasets to emphasize specific information. Common architectures trained on a generated starter dataset reached state-of-the-art performance on multiple common vision tasks and benchmarks.
  arXiv  Detail & Related papers  (2021-10-11T04:21:46Z)
• Mobile Sensing for Multipurpose Applications in Transportation [0.0]
  The State Departments of Transportation struggles to collect consistent data for analyzing and resolving transportation problems in a timely manner. Recent advancements in the sensors integrated into smartphones have resulted in a more affordable method of data collection. The developed app was evaluated by collecting data on the i70W highway connecting Columbia, Missouri, and Kansas City, Missouri.
  arXiv  Detail & Related papers  (2021-06-20T17:56:12Z)
• TAO: A Large-Scale Benchmark for Tracking Any Object [95.87310116010185]
  Tracking Any Object dataset consists of 2,907 high resolution videos, captured in diverse environments, which are half a minute long on average. We ask annotators to label objects that move at any point in the video, and give names to them post factum. Our vocabulary is both significantly larger and qualitatively different from existing tracking datasets.
  arXiv  Detail & Related papers  (2020-05-20T21:07:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.