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

Sun, Xiaoqiang; Zhang, Peng; Sookhak, Mehdi; Yu, Jianping; Xie, Weixin

doi:10.1007/s00779-017-1056-7

Cited by 22 publications

(12 citation statements)

References 28 publications

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“…However, the early approach lacks the much-needed security requirements. For the realization of security in smart healthcare, the author in [ 12 ] utilizes fully homomorphic encryption (FHE) to encrypt the data. For better security, Cai et al [ 13 ] create a novel medical record based on the mobile cloud without compromising too much performance.…”

Section: Related Work and Background Knowledgementioning

confidence: 99%

An Efficient and Secure Revocation-Enabled Attribute-Based Access Control for eHealth in Smart Society

Khan

Iqbal

Waheed

et al. 2022

Sensors

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The ever-growing ecosystem of the Internet of Things (IoT) integrating with the ever-evolving wireless communication technology paves the way for adopting new applications in a smart society. The core concept of smart society emphasizes utilizing information and communication technology (ICT) infrastructure to improve every aspect of life. Among the variety of smart services, eHealth is at the forefront of these promises. eHealth is rapidly gaining popularity to overcome the insufficient healthcare services and provide patient-centric treatment for the rising aging population with chronic diseases. Keeping in view the sensitivity of medical data, this interfacing between healthcare and technology has raised many security concerns. Among the many contemporary solutions, attribute-based encryption (ABE) is the dominant technology because of its inherent support for one-to-many transfer and fine-grained access control mechanisms to confidential medical data. ABE uses costly bilinear pairing operations, which are too heavy for eHealth’s tiny wireless body area network (WBAN) devices despite its proper functionality. We present an efficient and secure ABE architecture with outsourcing intense encryption and decryption operations in this work. For practical realization, our scheme uses elliptic curve scalar point multiplication as the underlying technology of ABE instead of costly pairing operations. In addition, it provides support for attribute/users revocation and verifiability of outsourced medical data. Using the selective-set security model, the proposed scheme is secure under the elliptic curve decisional Diffie–Hellman (ECDDH) assumption. The performance assessment and top-ranked value via the help of fuzzy logic’s evaluation based on distance from average solution (EDAS) method show that the proposed scheme is efficient and suitable for access control in eHealth smart societies.

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Section: Related Work and Background Knowledgementioning

confidence: 99%

An Efficient and Secure Revocation-Enabled Attribute-Based Access Control for eHealth in Smart Society

Khan

Iqbal

Waheed

et al. 2022

Sensors

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“…Akhbarifar et al (2020) proposed a remote health‐monitoring module that employed a lightweight block encryption technique to provide security for health and medicinal data in cloud based IoT platform. Sun et al (2017) proposed a common framework of the mobile healthcare network and determine three general secure medicinal computations that comprise average heart rate, the longer QT syndrome recognition, and chi‐square test. For attaining computations on the cipher text, they leverage FHE to encrypt healthcare data.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis

Alzubi

Beseiso

et al. 2021

Expert Systems

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Medical database classification problems can be considered as complex optimization problems to assure the diagnosis support precisely. In healthcare, several computer researchers have employed different deep learning (DL) approaches to enhance the classification performance. Besides, encryption is an effective way to offer secure transmission of medical data over public network. With this motivation, this paper presents new privacy‐preserving encryption with DL based medical data transmission and classification (PPEDL‐MDTC) model. The presented model derives multiple key‐based homomorphic encryption (MHE) technique with sailfish optimization (SFO), called MHE‐SFO algorithm‐based encryption process. In addition, the cross‐entropy based artificial butterfly optimization‐based feature selection technique and optimal deep neural network (ODNN) based classification is carried out. In ODNN model, the hyperparameter optimization of the DNN model is carried out utilizing the use of chemical reaction optimization (CRO) algorithm. The proposed method has been simulated utilizing Python 3.6.5 tool, which is tested using activity recognition and sleep stage dataset. A detailed comparative outcomes analysis makes sure the higher efficiency of the PPEDL‐MDTC on the state of art techniques with the detection accuracy of 0.9813 and 0.9650 on the applied activity recognition and University College Dublin Sleep Stage dataset.

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“…Then, the proposed scheme is applied for secure medical computations, which include comparison, Pearson goodness-of-fit test, and logistic regression. Sun et al [27] implemented secure average heart rate, long QT syndrome detection, and chi-square tests by using Dowlin et al's FHE scheme [28]. Based on Boneh et al's homomorphic encryption scheme [29], Poon et al [30] implemented the secure Fisher's exact test algorithm, which is often used to guarantee the statistical stability of genetic analysis.…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Reinforcement Learning Approach for Dynamic Treatment Regimes on Health Data

Sun

Wang

et al. 2021

Wireless Communications and Mobile Computing

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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.

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

Cited by 22 publications

References 28 publications

An Efficient and Secure Revocation-Enabled Attribute-Based Access Control for eHealth in Smart Society

An Efficient and Secure Revocation-Enabled Attribute-Based Access Control for eHealth in Smart Society

Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis

A Privacy-Preserving Reinforcement Learning Approach for Dynamic Treatment Regimes on Health Data

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