Abstract
Based on the clinical states of the patient, dynamic treatment regime technology can provide various therapeutic methods, which is helpful for medical treatment policymaking. Reinforcement learning is an important approach for developing this technology. In order to implement the reinforcement learning algorithm efficiently, the computation of health data is usually outsourced to the untrustworthy cloud server. However, it may leak, falsify, or delete private health data. Encryption is a common method for solving this problem. But the cloud server is difficult to calculate encrypted health data. In this paper, based on Cheon et al.’s approximate homomorphic encryption scheme, we first propose secure computation protocols for implementing comparison, maximum, exponentiation, and division. Next, we design a homomorphic reciprocal of square root protocol firstly, which only needs one approximate computation. Based on the proposed secure computation protocols, we design a secure asynchronous advantage actor-critic reinforcement learning algorithm for the first time. Then, it is used to implement a secure treatment decision-making algorithm. Simulation results show that our secure computation protocols and algorithms are feasible.
Highlights
As a recent healthcare tendency, personalized medicine [1] enables the patient to obtain early diagnoses, risk estimation, optimal treatments with low costs by using molecular and cellular analysis technologies, diagnosis results, genetic information, etc
Personalized medicine is usually implemented by the dynamic treatment regime technology [2, 3], which can provide various therapeutic methods according to the time-varying clinical states of the patient
In order to acquire the ciphertexts of historical data owners and wearable devices, middle ciphertext results during the execution of privacy-preserving A3C reinforcement learning algorithm and treatment decision-making algorithm (Section 6), A∗1 eavesdrops on the communication links among the entities in the system model
Summary
As a recent healthcare tendency, personalized medicine [1] enables the patient to obtain early diagnoses, risk estimation, optimal treatments with low costs by using molecular and cellular analysis technologies, diagnosis results, genetic information, etc. Because of the patient’s limited computation ability, health data are usually outsourced to the cloud server for implementing the reinforcement learning algorithm. Because the cloud server may be untrusted, it is likely that health data will be illegally accessed, forged, tampered, or discarded in the process of transmission and computation It may be harmful for personal privacy, economic interests, and even the security of human life. The cloud server can run the reinforcement learning algorithm on the encrypted health data perfectly by using homomorphic encryption without leaking patient privacy. We first design the homomorphic reciprocal of square root protocol, which needs only one approximate computation (2) Based on the proposed secure computation protocols, we design the secure A3C reinforcement learning algorithm for the first time We use it to implement a secure treatment decision-making algorithm (3) we simulate the proposed secure computation protocols and algorithms on the personal computer’s virtual machine.
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