| Paper ID | IFS-7.6 |
| Paper Title |
PRIVACY-PRESERVING OPTIMAL INSULIN DOSING DECISION |
| Authors |
Zuobin Ying, City University of Macau, China; Shuanglong Cao, Anhui University, China; Shengmin Xu, Singapore Management University,, Singapore; Ximeng Liu, Fuzhou University, China; Lingjuan Lyu, Cen Chen, Li Wang, Ant Group, China |
| Session | IFS-7: Information Hiding, Cryptography and Cybersecurity |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Information Forensics and Security: [ADP] Anonymization And Data Privacy |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Precision diagnosis and treatment are blending outcomes of machine learning and the Internet of Medical Things (IoMT). In the diabetes treatment, a medical center acts as a medical service provider (MSP) with patients data from IoMT devices. The MSP calculates the accurate dosage by importing the health index data into a corresponding decision-making model. However, the outsourcing unprotected patient data directly to the MSP suffers privacy leakage. In this paper, we propose a privacy-preserving optimal insulin dosing decision in the IoMT system (PIDM) to assist doctors in their decision making with the patients privacy. To achieve practicality and confidentiality simultaneously, we design a series of secure and efficient interactive protocols depending on additive secret sharing to perform in one stage of DQN, namely, optimal decision making. Contrasted to the most relevant schemes, no additional trusted party is needed in our PIDM, which makes our system more practical and efficient. The security of PIDM is testified, meanwhile, the system effectiveness, and the overall efficiency of PIDM is demonstrated through theoretical analysis and simulation experiments. |
