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Poster: Leveraging Apple's Find My Network for Large-Scale Distributed Sensing

Published: 04 June 2024 Publication History

Abstract

Find My is a crowd-sourced network of hundreds of millions of Apple devices that use Bluetooth Low Energy (BLE) to detect and track the location of items. We explore the limits and opportunities of using this proprietary network for large-scale distributed sensing. The key idea is to let low-cost sensing devices emit specially crafted BLE advertisements that trick nearby Apple devices into generating location reports that carry arbitrary sensor data, which can then be retrieved from the Apple servers. This paper reports on our ongoing work to reverse engineer the Find My system and to design a protocol for the efficient and reliable collection of data from sensing devices via the Find My network. Preliminary results from real-world experiments demonstrate the feasibility of our approach and a several-fold performance improvement compared with the state of the art.

References

[1]
A. Bellon, A. Yen, and P. Pannuto. 2023. TagAlong: Free, Wide-Area Data-Muling and Services. In HotMobile.
[2]
A. Heinrich, M. Stute, and M. Hollick. 2021. OpenHaystack: A Framework for Tracking Personal Bluetooth Devices via Apple's Massive Find My Network. In WiSec.
[3]
L. Tonetto, A. Carrara, A. Ding, and J. Ott. 2022. Where Is My Tag? Unveiling Alternative Uses of the Apple FindMy Service. In WoWMoM.

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Published In

cover image ACM Conferences
MOBISYS '24: Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services
June 2024
778 pages
ISBN:9798400705816
DOI:10.1145/3643832
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 June 2024

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Author Tags

  1. offline finding networks
  2. delay-tolerant networking
  3. reverse engineering
  4. crowd-sourced sensing
  5. find my
  6. apple

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  • Short-paper

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  • LOEWE-Zentrum emergenCITY

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MOBISYS '24
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Overall Acceptance Rate 274 of 1,679 submissions, 16%

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