Energy Efficient and Secure Neural Network–based Disease Detection Framework for Mobile Healthcare Network

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Sona Alex, Kirubai Dhanaraj, P. P. Deephi
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引用次数: 0

Abstract

Adopting mobile healthcare network (MHN) services such as disease detection is fraught with concerns about the security and privacy of the entities involved and the resource restrictions at the Internet of Things (IoT) nodes. Hence, the essential requirements for disease detection services are to (i) produce accurate and fast disease detection without jeopardizing the privacy of health clouds and medical users and (ii) reduce the computational and transmission overhead (energy consumption) of the IoT devices while maintaining the privacy. For privacy preservation of widely used neural network– (NN) based disease detection, existing literature suggests either computationally heavy public key fully homomorphic encryption (FHE), or secure multiparty computation, with a large number of interactions. Hence, the existing privacy-preserving NN schemes are energy consuming and not suitable for resource-constrained IoT nodes in MHN. This work proposes a lightweight, fully homomorphic, symmetric key FHE scheme (SkFhe) to address the issues involved in implementing privacy-preserving NN. Based on SkFhe, widely used non-linear activation functions ReLU and Leaky ReLU are implemented over the encrypted domain. Furthermore, based on the proposed privacy-preserving linear transformation and non-linear activation functions, an energy-efficient, accurate, and privacy-preserving NN is proposed. The proposed scheme guarantees privacy preservation of the health cloud’s NN model and medical user’s data. The experimental analysis demonstrates that the proposed solution dramatically reduces the overhead in communication and computation at the user side compared to the existing schemes. Moreover, the improved energy efficiency at the user is accomplished with reduced diagnosis time without sacrificing classification accuracy.
基于节能安全神经网络的移动医疗网络疾病检测框架
采用疾病检测等移动医疗网络(MHN)服务充满了对相关实体安全和隐私以及物联网(IoT)节点资源限制的担忧。因此,疾病检测服务的基本要求是(i)在不危害健康云和医疗用户隐私的情况下进行准确快速的疾病检测,以及(ii)在保持隐私的同时减少物联网设备的计算和传输开销(能耗)。为了保护广泛使用的基于神经网络(NN)的疾病检测的隐私,现有文献建议要么是计算量大的公钥全同态加密(FHE),要么是具有大量交互的安全多方计算。因此,现有的隐私保护神经网络方案是耗能的,不适合MHN中资源受限的物联网节点。本文提出了一种轻量级、全同态、对称密钥FHE方案(SkFhe),以解决实现隐私保护神经网络所涉及的问题。基于SkFhe,在加密域上实现了广泛使用的非线性激活函数ReLU和Leaky ReLU。此外,基于所提出的隐私保护线性变换和非线性激活函数,提出了一种节能、准确、隐私保护的神经网络。所提出的方案保证了健康云的NN模型和医疗用户数据的隐私保护。实验分析表明,与现有方案相比,所提出的解决方案显著降低了用户端的通信和计算开销。此外,在不牺牲分类精度的情况下,在减少诊断时间的情况下实现了用户能量效率的提高。
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来源期刊
ACM Transactions on Privacy and Security
ACM Transactions on Privacy and Security Computer Science-General Computer Science
CiteScore
5.20
自引率
0.00%
发文量
52
期刊介绍: ACM Transactions on Privacy and Security (TOPS) (formerly known as TISSEC) publishes high-quality research results in the fields of information and system security and privacy. Studies addressing all aspects of these fields are welcomed, ranging from technologies, to systems and applications, to the crafting of policies.
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