Mobile Healthcare Network Research Articles

Privacy-Preserving and Energy-Saving Random Forest-Based Disease Detection Framework for Green Internet of Things in Mobile Healthcare Networks

Privacy-Preserving and Energy-Saving Random Forest-Based Disease Detection Framework for Green Internet of Things in Mobile Healthcare Networks

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

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.

Private and Energy-Efficient Decision Tree-Based Disease Detection for Resource-Constrained Medical Users in Mobile Healthcare Network

In mobile healthcare networks (MHN), outsourced disease detection services demand the privacy preservation of medical users and health service providers (health clouds). This necessitates the use of a fully homomorphic encryption (FHE) while providing disease detection services, such as decision tree-based disease detection. However, the existing homomorphic encryption schemes utilized in decision tree-based disease detection that ensure the privacy of the medical user and health cloud are computationally-intensive and energy-hungry at the edge devices. Hence the medical user finds it difficult to exploit the existing private decision tree-based disease detection services due to restrictions on battery capacity and computing resources. Therefore, this work proposes a protocol for private decision tree classification with low resource consumption (PDTC-LRC) on edge devices of medical users by considering decision tree parameters as confidential to the health cloud. An energy-efficient, additively homomorphic, symmetric key-based FHE-compatible Rivest scheme (FCRS) is developed for implementing PDTC-LRC. FCRS can be decrypted homomorphically at the health cloud to support additive and multiplicative homomorphism. Also, an energy and bandwidth-efficient secure integer comparison protocol is developed for realizing PDTC-LRC. Experiments on the Raspberry Pi 3B+ board validate the improved energy efficiency and real-time applicability of the proposed secure integer comparison protocol and decision tree classifier compared with similar schemes available in the literature. Simulation and mathematical analysis ensure that user and health cloud privacy requirements are achieved by maintaining the classification accuracy same as that of decision tree classification in the plain domain.

SPCOR: a secure and privacy‐preserving protocol for mobile‐healthcare emergency to reap computing opportunities at remote and nearby

This study proposes a secure and privacy-preserving protocol for outsourcing health data processing operations during the emergency in the mobile healthcare network. The proposed protocol provides a practical solution to utilise smartphone resources at both remote and nearby for processing the overwhelming personal health information (PHI) of a user in healthcare emergency opportunistically and securely. The patients with symptoms matching with those of the user in an emergency are considered as opportunities to minimise the privacy disclosure of the user. Opportunities at both remote and nearby are exploited with the help of a base station in the 4G network. Moreover, novel and efficient outsourced privacy access control schemes are developed to minimise the power drain of the user in an emergency without compromising his privacy. The outsourced privacy access control is facilitated through the design of innovative schemes for outsourced attribute-based access mechanism and an outsourced privacy-preserving scalar product computation. Detailed performance evaluations through implementations on Raspberry Pi 3B + board and simulations using NS3 network simulator and Scyther tool confirm the efficiency of the proposed protocol in providing highly reliable PHI processing and transmissions with reasonably low delay and energy consumption while maintaining user privacy.

A Fast CP-ABE System for Cyber-Physical Security and Privacy in Mobile Healthcare Network

A healthcare network as a new context-aware cyber-physical system (CPS) includes embedded sensors, Internet of Things technology, and cloud computing. With the intellectualization of mobile terminal devices, mobile healthcare networks are automatically generated and can provide convenient healthcare services and real-time communication. However, two problems in the CPS need to resolved. A solution to the first problem is to mitigate the computational and storage cost of a mobile terminal. A solution to the second problem is to provide the cyber-physical privacy and security for healthcare data. Ciphertext-policy attribute-based encryption (CP-ABE) can provide data privacy and secure sharing in an open environment. In this article, a fast CP-ABE scheme is provided in order to satisfy the use of mobile terminal devices with low computational and storage power. In the proposed scheme, the work of a system can deliver expensive computational tasks to semitrusted third parties, ensuring a constant number of simple local computations. The scheme can also verify the correctness of the decryption result by using a Boneh–Lynn–Shacham short signature scheme. The CP-ABE scheme not only ensures the cyber-physical security of healthcare data, but also reduces the local load from the efficiency analysis and experimental results.

Verifiable keyword search for secure big data-based mobile healthcare networks with fine-grained authorization control

Mobile healthcare networks (MHNs) are increasingly viewed as potential applications for further improving the quality and efficiency of healthcare, with the rapid development of wearable devices. Wearable devices can generate a huge amount of health data, causing big data to be one of the most prominent features. The privacy and security of big data-based MHNs are major concerns of users, and these are the overriding obstacles that stand in the way of the wider adoption of MHNs. For the sake of security, health data is encrypted and stored on an untrusted server. However, the flexibility of the data is thereby affected, such as a search over encrypted health data, or authorization control for a search and verification of the search result. To address these problems, we propose a verifiable keyword search scheme for big data-based MHNs with fine-grained authorization control. In the proposed scheme, when sending the search request to the healthcare provider for the first time, the user needs to check whether he or she has the right to search within encrypted health data. Only authorized users can generate valid trapdoors for searching. Our verification technique is constructed based on an invertible Bloom lookup table and a Merkle hash tree, which can verify the completeness and correctness of the search result even if an empty set is returned by a dishonest healthcare provider. The security analysis shows that the proposed scheme is secure against chosen keyword attacks. The proposed scheme is efficient with low computation load, which can be used to perform eyword searches and verify the search results quickly in a big data environment.

Utilizing fully homomorphic encryption to implement secure medical computation in smart cities

As healthcare is one of major socioeconomic problems in cities, mobile healthcare network becomes one of core components of smart cities, which would improve urban healthcare environment. However, ...

Security and privacy for mobile healthcare networks: from a quality of protection perspective

With the flourishing of multi-functional wearable devices and the widespread use of smartphones, MHN becomes a promising paradigm of ubiquitous healthcare to continuously monitor our health conditions, remotely diagnose phenomena, and share health information in real time. However, MHNs raise critical security and privacy issues, since highly sensitive health information is collected, and users have diverse security and privacy requirements about such information. In this article, we investigate security and privacy protection in MHNs from the perspective of QoP, which offers users adjustable security protections at fine-grained levels. Specifically, we first introduce the architecture of MHN, and point out the security and privacy challenges from the perspective of QoP. We then present some countermeasures for security and privacy protection in MHNs, including privacy- preserving health data aggregation, secure health data processing, and misbehavior detection. Finally, we discuss some open problems and pose future research directions in MHNs.