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

Su-Mee Moon; Kim Jong Wook

doi:https://doi.org/10.9708/jksci.2020.25.10.059

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2020, 25(10), pp.59-67
DOI :https://doi.org/10.9708/jksci.2020.25.10.059
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

Su-Mee Moon ¹ , Kim Jong Wook ²

¹ 상명대학교
² 상명대학교

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  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 .
KEYWORDS

Health Data Collection , Data Privacy , Local Differential Privacy
REFERENCES (19)

[journal] Yang, Z. / 2016 / An IoT-cloud based wearable ECG monitoring system for smart healthcare / Journal of medical systems 40 (12) : 286~

[confproc] Hänsel, K. / 2015 / Challenges with current wearable technology in monitoring health data and providing positive behavioural support / Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare : 158~161

[journal] Chan, Y. F. Y. / 2018 / The asthma mobile health study, smartphone data collected using ResearchKit / Scientific data 5 : 180096~

[journal] Wang, S. / 2016 / Diagnosis code assignment using sparsity-based disease correlation embedding / IEEE Transactions on Knowledge and Data Engineering 28 (12) : 3191~3202

[journal] Dimiter V. Dimitrov / 2016 / Medical Internet of Things and Big Data in Healthcare / Healthcare Informatics Research / 대한의료정보학회 22 (3) : 156~163 / http://dx.doi.org/10.4258/hir.2016.22.3.156

[journal] Wu, P. Y. / 2016 / –Omic and electronic health record big data analytics for precision medicine / IEEE Transactions on Biomedical Engineering 64 (2) : 263~273

[journal] Manogaran, G. / 2018 / Health data analytics using scalable logistic regression with stochastic gradient descent / International Journal of Advanced Intelligence Paradigms 10 (1-2) : 118~132

[journal] Chaudhuri, S. / 2015 / Real world accuracy and use of a wearable fall detection device by older adults / Journal of the American Geriatrics Society 63 (11) : 2415~

[journal] Takeda, R. / 2009 / Gait analysis using gravitational acceleration measured by wearable sensors / Journal of biomechanics 42 (3) : 223~233

[journal] Nath, N. D. / 2017 / Ergonomic analysis of construction worker's body postures using wearable mobile sensors / Applied ergonomics 62 : 107~117

[other] Beaulieu-Jones, B. K. / 2018 / Privacy-preserving distributed deep learning for clinical data / arXiv preprint arXiv:1812.01484

[journal] Guan, Z. / 2019 / Achieving data utility-privacy tradeoff in Internet of medical things: A machine learning approach / Future Generation Computer Systems 98 : 60~68

[confproc] Mohammed, N. / 2015 / Secure and private management of healthcare databases for data mining / 2015 IEEE 28th International Symposium on Computer-Based Medical Systems / IEEE : 191~196

[journal] Tang, W. / 2019 / Secure data aggregation of lightweight e-healthcare iot devices with fair incentives / IEEE Internet of Things Journal 6 (5) : 8714~8726

[confproc] Qin, Z. / 2016 / Heavy hitter estimation over set-valued data with local differential privacy / Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security : 192~203

[journal] Kim, J. W. / 2018 / Application of local differential privacy to collection of indoor positioning data / IEEE Access 6 : 4276~4286

[confproc] McSherry, F. D. / 2009 / Privacy integrated queries: an extensible platform for privacy-preserving data analysis / Proceedings of the 2009 ACM SIGMOD International Conference on Management of data : 19~30

[journal] Kim, J. W. / 2018 / Privacy-preserving aggregation of personal health data streams / PloS one 13 (11)

[journal] 문수미 / 2020 / Privacy-Preserving Method to Collect Health Data from Smartband / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 25 (4) : 113~121 / https://doi.org/10.9708/jksci.2020.25.04.113
KCI Citation (1)

KJC Korea
Journal Central

Statistics

Tools