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Mind the tracker you wear: a security analysis of wearable health trackers

Published: 04 April 2016 Publication History

Abstract

Wearable tracking devices have gained widespread usage and popularity because of the valuable services they offer, monitoring human's health parameters and, in general, assisting persons to take a better care of themselves. Nevertheless, the security risks associated with such devices can represent a concern among consumers, because of the sensitive information these devices deal with, like sleeping patterns, eating habits, heart rate and so on. In this paper, we analyse the key security and privacy features of two entry level health trackers from leading vendors (Jawbone and Fitbit), exploring possible attack vectors and vulnerabilities at several system levels. The results of the analysis show how these devices are vulnerable to several attacks (perpetrated with consumer-level devices equipped with just bluetooth and Wi-Fi) that can compromise users' data privacy and security, and eventually call the tracker vendors to raise the stakes against such attacks.

References

[1]
M. B. Barcena, C. Wueest, and H. Lau. How Safe is Your Quantified Self. Symantec Security Response, August 2014.
[2]
Bluetooth SIG. Security, Bluetooth Smart (Low Energy). https://developer.bluetooth.org/TechnologyOverview/Pages/LE-Security.aspx {Retrieved 17/09/2015}.
[3]
Bluetooth SIG. The Low Energy Technology Behind Bluetooth Smart. http://www.bluetooth.com/Pages/low-energy-tech-info.aspx {Retrieved 17/09/2015}.
[4]
E. Clausing, M. Schiefer, and U. L. M. Morgenstern. Internet of Things - Security Evaluation of Nine Fitness Trackers. http://www.symantec.com/connect/blogs/how-safe-your-quantified-self-tracking-monitoring-and-wearable-tech {Posted 30/07/2014}.
[5]
R. W. Connor Tumbleson. An Assembler/Disassembler for Dex Format. https://github.com/JesusFreke/smali {Retrieved 17/09/2015}.
[6]
M. Conti, N. Dragoni, and S. Gottardo. MITHYS: Mind The Hand You Shake - Protecting Mobile Figure 7: Security Analysis of Fitness Trackers Devices from SSL Usage Vulnerabilities. In 9th International Workshop on Security and Trust Management (STM'13), Springer LNCS, 2013.
[7]
B. Cyr, W. Horn, D. Miao, and M. Specter. Security Analysis of Wearable Fitness Devices (Fitbit), 2014. https://courses.csail.mit.edu/6.857/2014/files/17-cyrbritt-webbhorn-specter-dmiao-hacking-fitbit.pdf {Retrieved 17/09/2015}.
[8]
M. Georgiev, S. Iyengar, S. Jana, R. Anubhai, D. Boneh, and V. Shmatikov. The Most Dangerous Code in the World: Validating SSL Certificates in Non-browser Software. In Proceedings of ACM CCS'12, pages 38--49. ACM, 2012.
[9]
M. Rahman, B. Carbunar, and M. Banik. Fit and Vulnerable: Attacks and Defenses for a Health Monitoring Device. arXiv preprint arXiv:1304.5672, 2013.
[10]
S. Stein. Best Wearable Tech of 2015. http://www.cnet.com/topics/wearable-tech/best-wearable-tech/{Posted 14/09/2015}.
[11]
J. Wolff. Get Started with Bluetooth Low Energy. http://www.jaredwolff.com/blog/get-started-with-bluetooth-low-energy/{Posted 14/04/2014}.
[12]
W. Zhou and S. Piramuthu. Security/Privacy of Wearable Fitness Tracking IoT Devices. In Proceedings of 9th Iberian Conference on Information Systems and Technologies (CISTI), pages 1--5. IEEE, 2014.

Cited By

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  • (2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 9-Apr-2024
  • (2024)Employing of machine learning and wearable devices in healthcare system: tasks and challengesNeural Computing and Applications10.1007/s00521-024-10197-z36:29(17829-17849)Online publication date: 6-Aug-2024
  • (2023)Chancen und Nutzen assistiver Technologien für Menschen mit kognitiven BeeinträchtigungenSozialer Fortschritt10.3790/sfo.72.11.86972:11(869-887)Online publication date: 1-Nov-2023
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cover image ACM Conferences
SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing
April 2016
2360 pages
ISBN:9781450337397
DOI:10.1145/2851613
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 04 April 2016

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Author Tags

  1. privacy
  2. security
  3. wearable health trackers

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  • Research-article

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SAC 2016
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SAC 2016: Symposium on Applied Computing
April 4 - 8, 2016
Pisa, Italy

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SAC '16 Paper Acceptance Rate 252 of 1,047 submissions, 24%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
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Cited By

View all
  • (2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 9-Apr-2024
  • (2024)Employing of machine learning and wearable devices in healthcare system: tasks and challengesNeural Computing and Applications10.1007/s00521-024-10197-z36:29(17829-17849)Online publication date: 6-Aug-2024
  • (2023)Chancen und Nutzen assistiver Technologien für Menschen mit kognitiven BeeinträchtigungenSozialer Fortschritt10.3790/sfo.72.11.86972:11(869-887)Online publication date: 1-Nov-2023
  • (2023)Understanding Fitness Tracker Users’ and Non-Users’ Requirements for Interactive and Transparent Privacy InformationExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544549.3585698(1-7)Online publication date: 19-Apr-2023
  • (2023)Signal Emulation Attack and Defense for Smart Home IoTIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.316970520:3(2040-2057)Online publication date: 1-May-2023
  • (2023)Risk Assessment in Smart Aging Care Systems: An Elderly-Centered Perspective2023 IEEE International Conference on Digital Health (ICDH)10.1109/ICDH60066.2023.00012(1-12)Online publication date: Jul-2023
  • (2022)Security Threats and Cryptographic Protocols for Medical WearablesMathematics10.3390/math1006088610:6(886)Online publication date: 10-Mar-2022
  • (2022)AntibIoTicJournal of Computer Security10.3233/JCS-21002730:5(689-725)Online publication date: 1-Jan-2022
  • (2022)Combating False Data Injection Attacks on Human-Centric Sensing ApplicationsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35345776:2(1-22)Online publication date: 7-Jul-2022
  • (2022)Scenario-Driven Device-to-Device Access Control in Smart Home IoT2022 IEEE 4th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA)10.1109/TPS-ISA56441.2022.00035(217-228)Online publication date: Dec-2022
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