BBNSF: Blockchain-Based Novel Secure Framework Using RP 2 -RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems.
Mohit KumarPriya MukherjeeSahil Vermanull KavitaManinder KaurS SinghMartyna KobielnikMarcin WoźniakJana ShafiMuhammad Fazal IjazPublished in: Sensors (Basel, Switzerland) (2022)
The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest-Shamir-Adleman (RP 2 -RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP 2 -RSA attains a 96.123% security level.
Keyphrases
- healthcare
- neural network
- health information
- global health
- machine learning
- deep learning
- end stage renal disease
- big data
- public health
- chronic kidney disease
- social media
- newly diagnosed
- ejection fraction
- electronic health record
- cystic fibrosis
- high glucose
- case report
- emergency department
- artificial intelligence
- multiple sclerosis
- blood pressure
- body mass index
- endothelial cells
- peritoneal dialysis
- data analysis
- mental health
- heart rate
- weight loss
- combination therapy
- room temperature
- drug induced
- body weight