OIDPR: Optimized insulin dosage via privacy‐preserving reinforcement learning

Publication Type

Journal Article

Publication Date

5-2021

Abstract

The precision of insulin dosage is essential in the process of diabetes treatment. The fact is providing precise dosage is almost impossible for clinicians since blood sugar levels are dynamically affected by many factors. Even though some auxiliary dosing systems have been proposed, the required real‐time physical data about the health situation of diabetics is still hard to synchronize to the end‐devices instantly. The traditional personalized drug delivery frameworks for accurate dosing of insulin always collect and transmit medical data in cleartext, which raises privacy problems. In this article, we propose a framework for an optimized insulin dosage via privacy‐preserving reinforcement learning to diabetics (OIDPR). In OIDPR, both the additive secret sharing and edge computing are deployed to achieve data confidentiality and performance optimization. The medical data is divided into multiple secret shares uniformly at random for outsourcing and operating at the edge servers. During the computation task of reinforcement learning, data is encrypted and processed via our proposed additive secret sharing protocol, where the privacy is reserved by the efficient encryption mechanism and the secret sharing system only incurs little workload. We provide comprehensive theoretical analyses and experimental results that demonstrate the supervisor functionality and high performance of our framework.

Keywords

additive secret sharing, individualization dosing delivery, privacy-preserving

Discipline

Medicine and Health Sciences | Software Engineering

Publication

Transactions on Emerging Telecommunications Technologies

Volume

32

Issue

5

First Page

1

Last Page

18

ISSN

2161-3915

Identifier

10.1002/ett.3953

Publisher

Wiley: 12 months

Additional URL

https://doi.org/10.1002/ett.3953

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