Your Signal, Their Data: An Empirical Privacy Analysis of Wireless-scanning SDKs in Android

2025-03-19

AI Summary

This paper exposes the covert exploitation of Android wireless scanning SDKs (BLE/WiFi) to harvest device identifiers (AAID), GPS coordinates, and wireless scan data, enabling cross-SDK data sharing and identifier bridging to construct persistent user mobility profiles—severely undermining long-term anonymity. We conduct the first empirical privacy analysis of 52 commercial SDKs, developing an automated pipeline integrating wireless event injection, dynamic instrumentation (Frida/adb), and network traffic capture. Our analysis reveals that 86% of apps integrating these SDKs transmit sensitive data, with multiple major vendors actively engaged in identifier synchronization and cross-app tracking. Key contributions include: (1) empirical validation that identifier bridging systematically breaks anonymity guarantees; and (2) three actionable mitigation strategies—sandbox hardening, strict policy enforcement, and transparent disclosure—to curb such abuses.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Mobile apps frequently use Bluetooth Low Energy (BLE) and WiFi scanning permissions to discover nearby devices like peripherals and connect to WiFi Access Points (APs). However, wireless interfaces also serve as a covert proxy for geolocation data, enabling continuous user tracking and profiling. This includes technologies like BLE beacons, which are BLE devices broadcasting unique identifiers to determine devices' indoor physical locations; such beacons are easily found in shopping centres. Despite the widespread use of wireless scanning APIs and their potential for privacy abuse, the interplay between commercial mobile SDKs with wireless sensing and beaconing technologies remains largely unexplored. In this work, we conduct the first systematic analysis of 52 wireless-scanning SDKs, revealing their data collection practices and privacy risks. We develop a comprehensive analysis pipeline that enables us to detect beacon scanning capabilities, inject wireless events to trigger app behaviors, and monitor runtime execution on instrumented devices. Our findings show that 86% of apps integrating these SDKs collect at least one sensitive data type, including device and user identifiers such as AAID, email, along with GPS coordinates, WiFi and Bluetooth scan results. We uncover widespread SDK-to-SDK data sharing and evidence of ID bridging, where persistent and resettable identifiers are shared and synchronized within SDKs embedded in applications to potentially construct detailed mobility profiles, compromising user anonymity and enabling long-term tracking. We provide evidence of key actors engaging in these practices and conclude by proposing mitigation strategies such as stronger SDK sandboxing, stricter enforcement of platform policies, and improved transparency mechanisms to limit unauthorized tracking.

Problem

• Analyzes privacy risks of wireless-scanning SDKs in Android apps.
• Investigates data collection practices and user tracking via BLE and WiFi.
• Proposes mitigation strategies to enhance user privacy and prevent unauthorized tracking.

Innovation

• Systematic analysis of 52 wireless-scanning SDKs
• Developed pipeline for detecting beacon scanning capabilities
• Proposed mitigation strategies against unauthorized tracking