An EnergyEfficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study
Abstract
:1. Introduction
 Leveraging model driven prediction, encryption, and data points (DP) with edge computing to propose a twotier privacypreserving IoHT framework that does not currently exist.
 Evaluation of the proposed system in terms of efficiency and privacy preservation with up to 98.83% and 95.95% of data savings rate (SR) and accuracy rate (AR), respectively, while maintaining sufficient accuracy that is arbitrarily required by users.
 Presenting potential application scenarios that would benefit from this solution.
2. Related Works
2.1. WBAN and IoT Networks
2.2. Health Inference and Prediction Analysis
2.3. Privacy Preservation
2.3.1. CryptographyBased Schemes
2.3.2. Differential PrivacyBased Schemes
3. The Proposed Solution
3.1. The First Tier Data Reduction Using a Data Inference Algorithm
Algorithm 1: Variance rate algorithm initialization; 

3.2. The Second Tier Data Protection with Differential Privacy
Definition of Differential Privacy
4. Results and Analysis
4.1. Efficiency and Accuracy Evaluation
4.2. Privacy Preservation Evaluation
5. Beneficial Applications
5.1. Patient Monitoring of Disease Outbreak
5.2. Battery Conservation of Personal Health Devices
5.3. Health Data for Identificationes
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Cases  Evaluation Condition  DP  Savings (%)  Accuracy (%) 

Case 0  Original data  1800  N/A  N/A 
Case 1  Removed duplication  1716  4.67  99.74 
Case 2  Beacon Interval = 30 s  31  98.27  96.26 
Case 3  Beacon Interval = 60 s  16  99.11  95.73 
Case 4  Beacon Interval = 120 s  9  99.50  94.18 
Case 5  Beacon Interval = 180 s  6  99.66  94.21 
Case 6  Variance Rate (2%) with Beacon Interval = 60 s  182  89.88  97.57 
Case 7  Variance Rate (3%) with Beacon Interval = 60 s  64  36.44  36.04 
Case 8  Variance Rate (10%) with Beacon Interval = 60 s  22  98.78  96.14 
Case 9  Variance Rate (15%) with Beacon Interval = 60 s  21  98.83  95.95 
Inferred rate  0  1.5%  2%  3%  5%  10%  15% 
Data points  6720  4000  3559  3002  2074  1055  587 
Savings (%)  0  40.7  47.0  55.3  69.1  84.3  91.2 
Accuracy (%)  N/A  98.3  97.0  97.2  95.6  90.5  86.6 
VR  0%  2.5%  5%  10%  20% 
DP  1420  691  306  146  17 
Saving (%)  N/A  51.3  78.5  89.7  98.8 
The value of $\epsilon $  0.01  0.05  0.1  0.2  0.5  1.0 
Average value of raw data  73.76  73.76  73.76  73.76  73.76  73.76 
Average value of modified data  73.84  73.18  73.14  73.89  73.76  73.76 
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Kang, J.J.; Dibaei, M.; Luo, G.; Yang, W.; HaskellDowland, P.; Zheng, X. An EnergyEfficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study. Sensors 2021, 21, 312. https://doi.org/10.3390/s21010312
Kang JJ, Dibaei M, Luo G, Yang W, HaskellDowland P, Zheng X. An EnergyEfficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study. Sensors. 2021; 21(1):312. https://doi.org/10.3390/s21010312
Chicago/Turabian StyleKang, James Jin, Mahdi Dibaei, Gang Luo, Wencheng Yang, Paul HaskellDowland, and Xi Zheng. 2021. "An EnergyEfficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study" Sensors 21, no. 1: 312. https://doi.org/10.3390/s21010312