Table 1.

The summary of related works.

Reference	Methods	Systems	Key Results	Advantages	Limitations
Thilagam, K. et al. [30]	CNN	Private healthcare data	Accuracy, Recall, F1-score, Precision, False Alarm Rate, and Missed Detection Rate.	Data integrity and privacy leaks are both minimal.	High time and cost consumption.
Ali, Aitizaz et al. [31]	homomorphic encryption	Digital healthcare	Throughput, encryption time, decryption time, latency, and computational cost.	Gives consumers more flexibility.	The loss function is high.
Kumar, Randhir et al. [32]	PBDL (SSVAE and SA-BiLSTM)	Industrial healthcare	Transmission efficiency, encryption and decryption time, accuracy, and loss.	Secured authenticated data transmission and attack detection.	More extended training period and slower prototype.
Kute, Shruti Suhas et al. [33]	Machine learning	IoT-based healthcare	Accuracy, loss, and confidential rate.	Effective validation is achieved for different sickness.	Need to address real-time challenges.
Ali, Aitizaz et al. [34]	GT-BSS	Digital healthcare	Throughput, encryption time, decryption time, latency, and computational cost.	Limits security problems to patient data.	Very low test accuracy.
Anuradha, M. et al. [35]	AES	Cancer prediction system	Cost and time.	High-security function.	A small amount of data is considered for validation.
Satyanarayana, T.V.V. et al. [36]	BSN	Medical system	CPU cycles and execution time.	Security needs are effectively solved.	Very few metrics are validated for the performance evaluation.
Zulkifl, Z., Khan et al. [37]	FBASHI	Hospital department	Latency and throughput.	Different kinds of attacks are evaluated.	Lack of evaluation metrics.
Das, S. and Namasudra [38].	Lightweight cryptographic primitives	Healthcare center	Computation cost and execution time.	Feasible for lightweight and low resource IoT gadgets.	Only applicable for low resource IoT gadgets and systems. Additionally, less data.