A Nearest Neighbour-Based Analysis to Identify Patients from Continuous Glucose Monitor Data

  • Michael MayoEmail author
  • Vithya Yogarajan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)


Continuous glucose monitors (CGMs) are minimally invasive sensors that detect blood glucose levels (usually in patients with diabetes) at high frequency. The devices produce considerable volumes of sensor data when used for weeks and months. We consider the following research question: is it possible to uniquely identify a patient from a fragment of their CGM data? That is, supposing a patient’s medical records are stored in a database along with a large sample of their CGM data, could an attacker with a much smaller sample of data from a different time period match the two time series and positively identify the patient? If the answer is yes, then significant patient privacy concerns are raised since many health records are now stored online. Our investigations using existing public CGM datasets reveal that many subjects can be uniquely identified using a simple nearest neighbour-based analysis approach.


Continuous glucose monitors Diabetes Nearest neighbour analysis Time series data Privacy Medical Internet of Things Data security 


  1. 1.
    Bailey, T.S., Chang, A., Christiansen, M.: Clinical accuracy of a continuous glucose monitoring system with an advanced algorithm. J. Diabetes Sci. Technol. 9(2), 209–214 (2014)CrossRefGoogle Scholar
  2. 2.
    Bruen, D., Delaney, C., Florea, L., Diamond, D.: Glucose sensing for diabetes monitoring: recent developments. Sensors 17(8), 1866 (2017)CrossRefGoogle Scholar
  3. 3.
    Buckingham, B., et al.: Effectiveness of early intensive therapy on \(\beta \)-cell preservation in type 1 diabetes. Diabetes Care 36, 4030 (2013)CrossRefGoogle Scholar
  4. 4.
    Cappon, G., Acciaroli, G., Vettoretti, M., Facchinetti, A., Sparacino, G.: Wearable continuous glucose monitoring sensors: a revolution in diabetes treatment. Electronics 6(3), 65 (2017)CrossRefGoogle Scholar
  5. 5.
    Hassanalieragh, M., et al.: Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: opportunities and challenges. In: 2015 IEEE International Conference on Services Computing (SCC), pp. 285–292. IEEE (2015)Google Scholar
  6. 6.
    Kovatchev, B.P., Cox, D.J., Gonder-Frederick, L.A., Clarke, W.: Symmetrization of the blood glucose measurement scale and its applications. Diabetes Care 20(11), 1655–1658 (1997)CrossRefGoogle Scholar
  7. 7.
    Kovatchev, B.P., Otto, E., Cox, D., Gonder-Frederick, L., Clarke, W.: Evaluation of a new measure of blood glucose variability in diabetes. Diabetes Care 29(11), 2433–2438 (2006)CrossRefGoogle Scholar
  8. 8.
    Mauras, N., et al.: A randomized clinical trial to assess the efficacy and safety of real-time continuous glucose monitoring in the management of type 1 diabetes in young children aged 4 to \(<\)10 years. Diabetes Care 35, 204 (2011)CrossRefGoogle Scholar
  9. 9.
    Pérez-Gandía, C., et al.: Decision support in diabetes care: the challenge of supporting patients in their daily living using a mobile glucose predictor. J. Diabetes Sci. Technol. 12(2), 243–250 (2018)CrossRefGoogle Scholar
  10. 10.
    Rodbard, D.: Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes. Diabetes Technol. Ther. 19(S3), S25 (2017)CrossRefGoogle Scholar
  11. 11.
    The Nightscout Foundation: About the Nightscout Data Commons on Open Humans (2014).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand

Personalised recommendations