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Collecting Health Data from Wearable Devices by Leveraging Salient Features in a Privacy-Preserving Manner

  • Moon, Su-Mee (Dept. of Computer Science, Sangmyung University) ;
  • Kim, Jong-Wook (Dept. of Computer Science, Sangmyung University)
  • Received : 2020.08.11
  • Accepted : 2020.09.20
  • Published : 2020.10.30

Abstract

With the development of wearable devices, individuals' health status can be checked in real time and risks can be predicted. For example, an application has been developed to detect an emergency situation of a patient with heart disease and contact a guardian through analysis of health data such as heart rate and electrocardiogram. However, health data is seriously damaging when it is leaked as it relates to life. Therefore, a method to protect personal information is essential in collecting health data, and this study proposes a method of collecting data while protecting the personal information of the data owner through a LDP(Local Differential Privacy). The previous study introduced a technique of transmitting feature point data rather than all data to a data collector as an algorithm for searching for fixed k feature points. Next, this study will explain how to improve the performance by up to 75% using an algorithm that finds the optimal number of feature points k.

웨어러블 기기의 발전으로 개인의 건강 상태를 실시간으로 확인하고 위험을 예측할 수 있게 되었다. 예를 들어 심장 질환 환자의 심박수, 심전도가 이상 수치를 보이면 위급 상황을 감지하여 자동으로 보호자에게 연락한다. 이처럼 즉각적인 대처를 가능케 하는 건강 데이터는 생명에 관계되는 만큼 유출되었을 시 심각한 피해를 발생시킨다. 본 연구는 지역 차분 프라이버시 기법을 통해 데이터 소유자의 개인 정보를 보호하면서 데이터를 수집하는 방법을 제안한다. 선행 연구에서는 고정된 k개의 특징 점을 탐색하는 알고리즘으로 전체 데이터가 아닌 특징 점 데이터를 데이터 수집가에게 전송하는 기법을 소개하였다. 이어서 본 연구는 최적의 특징 점 개수 k를 찾는 알고리즘을 이용하여 성능을 최대 75% 향상시키는 방법에 대해 설명할 것이다.

Keywords

References

  1. Yang, Z., Zhou, Q., Lei, L., Zheng, K., & Xiang, W. (2016). An IoT-cloud based wearable ECG monitoring system for smart healthcare. Journal of medical systems, 40(12), 286. https://doi.org/10.1007/s10916-016-0644-9
  2. Hansel, K., Wilde, N., Haddadi, H., & Alomainy, A. (2015, December). Challenges with current wearable technology in monitoring health data and providing positive behavioural support. In Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare (pp. 158-161).
  3. Chan, Y. F. Y., Bot, B. M., Zweig, M., Tignor, N., Ma, W., Suver, C., ... & Wang, P. (2018). The asthma mobile health study, smartphone data collected using ResearchKit. Scientific data, 5, 180096.
  4. Wang, S., Chang, X., Li, X., Long, G., Yao, L., & Sheng, Q. Z. (2016). Diagnosis code assignment using sparsity-based disease correlation embedding. IEEE Transactions on Knowledge and Data Engineering, 28(12), 3191-3202. https://doi.org/10.1109/TKDE.2016.2605687
  5. Dimitrov, D. V. (2016). Medical internet of things and big data in healthcare. Healthcare informatics research, 22(3), 156-163. https://doi.org/10.4258/hir.2016.22.3.156
  6. Wu, P. Y., Cheng, C. W., Kaddi, C. D., Venugopalan, J., Hoffman, R., & Wang, M. D. (2016). -Omic and electronic health record big data analytics for precision medicine. IEEE Transactions on Biomedical Engineering, 64(2), 263-273. https://doi.org/10.1109/TBME.2016.2573285
  7. Manogaran, G., & Lopez, D. (2018). Health data analytics using scalable logistic regression with stochastic gradient descent. International Journal of Advanced Intelligence Paradigms, 10(1-2), 118-132. https://doi.org/10.1504/IJAIP.2018.089494
  8. Chaudhuri, S., Oudejans, D., Thompson, H. J., & Demiris, G. (2015). Real world accuracy and use of a wearable fall detection device by older adults. Journal of the American Geriatrics Society, 63(11), 2415. https://doi.org/10.1111/jgs.13804
  9. Takeda, R., Tadano, S., Todoh, M., Morikawa, M., Nakayasu, M., & Yoshinari, S. (2009). Gait analysis using gravitational acceleration measured by wearable sensors. Journal of biomechanics, 42(3), 223-233. https://doi.org/10.1016/j.jbiomech.2008.10.027
  10. Nath, N. D., Akhavian, R., & Behzadan, A. H. (2017). Ergonomic analysis of construction worker's body postures using wearable mobile sensors. Applied ergonomics, 62, 107-117. https://doi.org/10.1016/j.apergo.2017.02.007
  11. Beaulieu-Jones, B. K., Yuan, W., Finlayson, S. G., & Wu, Z. S. (2018). Privacy-preserving distributed deep learning for clinical data. arXiv preprint arXiv:1812.01484.
  12. Guan, Z., Lv, Z., Du, X., Wu, L., & Guizani, M. (2019). Achieving data utility-privacy tradeoff in Internet of medical things: A machine learning approach. Future Generation Computer Systems, 98, 60-68. https://doi.org/10.1016/j.future.2019.01.058
  13. Mohammed, N., Barouti, S., Alhadidi, D., & Chen, R. (2015, June). Secure and private management of healthcare databases for data mining. In 2015 IEEE 28th International Symposium on Computer-Based Medical Systems (pp. 191-196). IEEE.
  14. Tang, W., Ren, J., Deng, K., & Zhang, Y. (2019). Secure data aggregation of lightweight e-healthcare iot devices with fair incentives. IEEE Internet of Things Journal, 6(5), 8714-8726. https://doi.org/10.1109/JIOT.2019.2923261
  15. Qin, Z., Yang, Y., Yu, T., Khalil, I., Xiao, X., & Ren, K. (2016, October). Heavy hitter estimation over set-valued data with local differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (pp. 192-203).
  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. McSherry, F. D. (2009, June). Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data (pp. 19-30).
  18. Kim, J. W., Jang, B., & Yoo, H. (2018). Privacy-preserving aggregation of personal health data streams. PloS one, 13(11).
  19. Moon, S. M., & Kim, J. W. (2020). Privacy-Preserving Method to Collect Health Data from Smartband. Journal of The Korea Society of Computer and Information, 25(4), 113-121.

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