Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Privacy-Preserving Method to Collect Health Data from Smartband

  • Moon, Su-Mee (Dept. of Computer Science, Sangmyung University) ;
  • Kim, Jong-Wook (Dept. of Computer Science, Sangmyung University)
  • Received : 2020.02.24
  • Accepted : 2020.04.16
  • Published : 2020.04.29
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Abstract

With the rapid development of information and communication technology (ICT), various sensors are being embedded in wearable devices. Consequently, these devices can continuously collect data including health data from individuals. The collected health data can be used not only for healthcare services but also for analyzing an individual's lifestyle by combining with other external data. This helps in making an individual's life more convenient and healthier. However, collecting health data may lead to privacy issues since the data is personal, and can reveal sensitive insights about the individual. Thus, in this paper, we present a method to collect an individual's health data from a smart band in a privacy-preserving manner. We leverage the local differential privacy to achieve our goal. Additionally, we propose a way to find feature points from health data. This allows for an effective trade-off between the degree of privacy and accuracy. We carry out experiments to demonstrate the effectiveness of our proposed approach and the results show that, with the proposed method, the error rate can be reduced upto 77%.

센서 기술의 발전과 스마트 워치, 스마트 밴드와 같은 웨어러블 기기의 보편화로 개인의 건강데이터를 실시간으로 수집하는 일이 가능해졌다. 웨어러블 기기에서 파생된 걸음 수, 심박 수와 같은 건강 데이터들은 모바일 환경의 위치, 날씨 데이터 등의 외부 데이터와 결합하여, 개인의 라이프 스타일 및 건강 상태를 분석하는 방식으로 활용되고 있다. 이처럼 웨어러블 기기에서 파생된 건강 데이터는 편리하고 유용한 기능을 제공하지만 개인의 생활과 밀접한 연관이 있기 때문에 외부에 노출될 경우 심각한 프라이버시 침해 문제가 발생한다. 이에 본 연구는 지역차분프라이버시와 특징점 추출 알고리즘을 사용하여, 웨어러블 기기에서 추출한 건강 데이터를 데이터 소유자의 프라이버시 침해 없이 데이터 수집가에게 전송할 수 있는 기법을 소개한다. 지역차분프라이버시를 통해 데이터 소유자의 프라이버시를 보호하였으며 특징점 알고리즘으로 프라이버시 보호 수준과 데이터 유용성간의 상충 관계를 조절하였다. 실험 결과는 제안하는 기법이 단순 방법에 비해 최대 77% 정도의 오차율 개선이 있음을 보여준다. 수집된 데이터는 데이터 사용자의 요구에 따라 헬스 케어 및 맞춤형 서비스 산업에서 유의미하게 활용될 수 있다.

Keywords

References

  1. R. Benzo, "Activity monitoring in chronic obstructive pulmonary disease," Journal of cardiopulmonary rehabilitation and prevention, vol. 29, p. 341, 2009. https://doi.org/10.1097/HCR.0b013e3181be7a3c
  2. B. Chikhaoui, B. Ye, and A. Mihailidis, "Ensemble Learning-Based Algorithms for Aggressive and Agitated Behavior Recognition," in Ubiquitous Computing and Ambient Intelligence: 10th International Conference, UCAmI 2016, San Bartolome de Tirajana, Gran Canaria, Spain, November 29-December 2, 2016, Part II, 2016, pp. 9-20.
  3. K. Missen, J. E. Porter, A. Raymond, K. de Vent, and J.- A. Larkins, "Adult deterioration detection system (adds): An evaluation of the impact on met and code blue activations in a regional healthcare service," Collegian, 2017.
  4. M. M. M. Fouad, N. El-Bendary, R. A. Ramadan, and A. E. Hassanien, "Wireless sensor networks: a medical perspective," Wireless Sensor Networks: From Theory to Applications, 2013.
  5. K. F. Navarro, E. Lawrence, and B. Lim, "Medical mote- care: A distributed personal healthcare monitoring sys- tem," in eHealth, Telemedicine, and Social Medicine, 2009. eTELEMED'09. International Conference on. IEEE, 2009, pp. 25-30.
  6. Manogaran, G., Varatharajan, R., Lopez, D., Kumar, P. M., Sundarasekar, R., & Thota, C. (2018). A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Generation Computer Systems, 82, 375-387. https://doi.org/10.1016/j.future.2017.10.045
  7. J. Taelman, S. Vandeput, A. Spaepen, and S. Van Huffel. Influence of mental stress on heart rate and heart rate variability. In J. Vander Sloten, P. Verdonck, M. Nyssen, and J. Haueisen, editors, 4th European Conference of the International Federation for Medical and Biological Engineering, pages 1366-1369, Berlin, Heidelberg, 2009. Springer Berlin Heidelberg.
  8. Fisher, R., Smailagic, A., & Sokos, G. (2017, December). Monitoring Health Changes in Congestive Heart Failure Patients Using Wearables and Clinical Data. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1061-1064). IEEE.
  9. Warburton, D. E., & Bredin, S. S. (2019). Health Benefits of Physical Activity: A Strengths-Based Approach.
  10. Ruegsegger, G. N., & Booth, F. W. (2018). Health benefits of exercise. Cold Spring Harbor perspectives in medicine, 8(7), a029694. https://doi.org/10.1101/cshperspect.a029694
  11. Altun, K., Barshan, B., & Tuncel, O. (2010). Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognition, 43(10), 3605-3620. https://doi.org/10.1016/j.patcog.2010.04.019
  12. P. Melillo, R. Castaldo, G. Sannino, A. Orrico, G. de Pietro, and L. Pecchia, "Wearable technology and ecg processing for fall risk assessment, prevention and detection," in Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 7740-7743.
  13. Ye, Q., Hu, H., Meng, X., & Zheng, H. (2019, May). PrivKV: Key-value data collection with local differential privacy. In 2019 IEEE Symposium on Security and Privacy (SP) (pp. 317-331). IEEE.
  14. Lim, J. H., & Kim, J. W. (2019). Privacy-Preserving IoT Data Collection in Fog-Cloud Computing Environment. Journal of the Korea Society of Computer and Information, 24(9), 43-49. https://doi.org/10.9708/JKSCI.2019.24.09.043
  15. Zhang, Z., Wang, T., Li, N., He, S., & Chen, J. (2018, January). Calm: Consistent adaptive local marginal for marginal release under local differential privacy. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (pp. 212-229).
  16. Kim, J. W., Kim, D. H., & Jang, B. (2018). Application of local differential privacy to collection of indoor positioning data. IEEE Access, 6, 4276-4286. https://doi.org/10.1109/access.2018.2791588
  17. Kim, J. W., Lim, J. H., Moon, S. M., & Jang, B. (2019). Collecting Health Lifelog Data From Smartwatch Users in a Privacy-Preserving Manner. IEEE Transactions on Consumer Electronics, 65(3), 369-378. https://doi.org/10.1109/tce.2019.2924466
  18. Kim, J. W., Jang, B., & Yoo, H. (2018). Privacy-preserving aggregation of personal health data streams. PloS one, 13(11).