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

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

Privacy-Preserving IoT Data Collection in Fog-Cloud Computing Environment

  • Received : 2019.06.24
  • Accepted : 2019.08.26
  • Published : 2019.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Today, with the development of the internet of things, wearable devices related to personal health care have become widespread. Various global information and communication technology companies are developing various wearable health devices, which can collect personal health information such as heart rate, steps, and calories, using sensors built into the device. However, since individual health data includes sensitive information, the collection of irrelevant health data can lead to personal privacy issue. Therefore, there is a growing need to develop technology for collecting sensitive health data from wearable health devices, while preserving privacy. In recent years, local differential privacy (LDP), which enables sensitive data collection while preserving privacy, has attracted much attention. In this paper, we develop a technology for collecting vast amount of health data from a smartwatch device, which is one of popular wearable health devices, using local difference privacy. Experiment results with real data show that the proposed method is able to effectively collect sensitive health data from smartwatch users, while preserving privacy.

Keywords

References

  1. Farahani, B., Firouzi, F., Chang, V., Badaroglu, M., Constant, N., & Mankodiya, K. (2018). Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. (pp.661). Future Generation Computer Systems, 78, 659-676. https://doi.org/10.1016/j.future.2017.04.036
  2. AbuKhousa, E., Mohamed, N., & Al-Jaroodi, J. (2012). e-Health cloud: opportunities and challenges. (pp.623). Future internet, 4(3), 621-645. https://doi.org/10.3390/fi4030621
  3. Samani, A., Ghenniwa, H. H., & Wahaishi, A. (2015). Privacy in Internet of Things: A model and protection framework. (pp.606). Procedia Computer Science, 52, 606-613. https://doi.org/10.1016/j.procs.2015.05.046
  4. Lu, R., Heung, K., Lashkari, A. H., & Ghorbani, A. A. (2017). A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT. IEEE Access, 5, 3302-3312. https://doi.org/10.1109/ACCESS.2017.2677520
  5. Dwork, C. (2011). Differential privacy. Encyclopedia of Cryptography and Security, 338-340.
  6. Kim, J. W., Lim, J. H., Moon, S. M., Yoo, H., & Jang, B. (2019, January). Privacy-Preserving Data Collection Scheme on Smartwatch Platform. In 2019 IEEE International Conference on Consumer Electronics (ICCE) pp. 2. IEEE.
  7. Wang, T., Blocki, J., Li, N., & Jha, S. (2017). Locally differentially private protocols for frequency estimation. pp. 729. In 26th {USENIX} Security Symposium ({USENIX} Security 17) (pp. 729-745).
  8. Du, W., & Zhan, Z. (2003, August). Using randomized response techniques for privacy-preserving data mining. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 505-510). ACM.
  9. Kim, J. W., Kim, D. H., & Jang, B. (2018). Application of local differential privacy to collection of indoor positioning data. pp. 1. IEEE Access, 6, 4276-4286. https://doi.org/10.1109/ACCESS.2018.2791588
  10. Erlingsson, U., Pihur, V., & Korolova, A. (2014, November). Rappor: Randomized aggregatable privacy-preserving ordinal response. In Proceedings of the 2014 ACM SIGSAC conference on computer and communications security (pp. 1054-1067). ACM.
  11. Du, W., & Zhan, Z. (2003, August). Using randomized response techniques for privacy-preserving data mining. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 505-510). ACM.
  12. Bonomi, F., Milito, R., Natarajan, P., & Zhu, J. (2014). Fog computing: A platform for internet of things and analytics. In Big data and internet of things: A roadmap for smart environments (pp. 169-186). Springer, Cham.
  13. Stojmenovic, I., & Wen, S. (2014, September). The fog computing paradigm: Scenarios and security issues. In 2014 Federated Conference on Computer Science and Information Systems (pp. 1-8). IEEE.
  14. Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012, August). Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing (pp. 13-16). ACM.
  15. Masip-Bruin, X., Marin-Tordera, E., Alonso, A., & Garcia, J. (2016, June). Fog-to-cloud computing (F2C): The key technology enabler for dependable e-health services deployment. In 2016 Mediterranean ad hoc networking workshop (Med-Hoc-Net) (pp. 1-5). IEEE.

Cited by

  1. Privacy-Preserving Method to Collect Health Data from Smartband vol.25, pp.4, 2020, https://doi.org/10.9708/jksci.2020.25.04.113
  2. Privacy-Preserving Traffic Volume Estimation by Leveraging Local Differential Privacy vol.26, pp.12, 2019, https://doi.org/10.9708/jksci.2021.26.12.019