Paper No	Data Acquisition	Applications	Parameter	Advantage	Disadvantage	Solution
1	EHR	IOT		Blockchain-based EHR sharing gives patients greater control over their health records, allowing them to manage access and permissions securely.	Implementing blockchain-based EHR systems requires a good understanding of blockchain technology and may be complex for healthcare organizations with limited expertise.	Creating user-friendly interfaces and tools for patients and healthcare providers can simplify the use of blockchain-based EHR systems.
2	Real Time	IOT Based Health Care		helps in understanding user attitudes towards IoT-based healthcare and the impact of privacy and security on trust.	Privacy and security concerns associated with IoT can affect users’ trust in using IoT devices.	Manufacturers should implement transparent privacy measures in IoT devices and educate users about them to build trust.
3	Real-Time	Consumer IoT		The research highlights IoT device privacy and security risks, encouraging a proactive response.	As IoT devices become more pervasive, the risk of privacy and security breaches may rise.	Continuous monitoring and regular updates of IoT devices can help mitigate evolving security threats.
4	Real Time	IOT		The challenges of health data anonymization and the potential for unauthorized access to IoT data.	IoT devices are vulnerable due to factors such as inexperienced manufacturers and inadequate security measures.	Requires data security training and must embed strong encryption and security features into IoT devices.
5	Eron Email Dataset	Healthcare Big Data		The Spark system efficiently manages large healthcare datasets, improving data processing speed and scalability.	Implementing Apache Spark and advanced anonymization techniques may be technically challenging for some organizations.	Invest in optimizing the computational resources required for Spark and anonymization processes.
6	Real Parking Dataset	Smart Parking System	Time stamp	K-anonymity and differential privacy techniques protect user privacy in parking recommender systems.	Anonymization techniques can impact the quality and utility of data.	Research and develop methods to balance privacy protection with data utility.
7	TELE ECG Database	Cloud-IoT	Minimum number of proxy servers in a homomorphic encryption.	The tuple partitioning approach effectively preserves privacy over incremental databases on the cloud.	Implementing tuple partitioning and packetization may be complex.	Explore simplified implementations of privacy-preserving strategies for broader use.
8	Mockaroo Database	Data Mining		The Efficient Anonymous Algorithm (NEAA) provides enhanced privacy for private data.	Implementing privacy algorithms may require technical expertise.	Continuously refine NEAA and similar algorithms to improve privacy protection.
9	Electronic Health Record (EHR)	Healthcare	System parameters	A prototype demonstrates the practical applicability of the established protocol.	Implementing mutual anonymous authentication protocols may require technical expertise.	Offer training and resources to facilitate the implementation of authentication protocols.
10	Eron Email Dataset	Blockchain	Security parameter	The proposed bloom filter-enabled multi-keyword search protocol emphasizes privacy preservation. By reducing the exposure of intermediate results, it minimizes the risk of service peers or other entities accessing sensitive information associated with the encrypted keywords.	While the protocol shows promise in a simulated environment, its real-world applicability and robustness may need further validation and testing in actual blockchain systems, which could present unforeseen challenges.	To address the complexity issue, clear and comprehensive documentation, along with training resources, should be made available to facilitate the implementation and operation of the protocol.
11	Adult dataset from UCI Machine Learning Repository	Cloud Computing		Scalability is a key advantage, particularly in cloud environments where data can be distributed across multiple storage nodes.	While the approach offers scalability and efficiency, it may also introduce complexity due to the need for indexing and specialized algorithms.	user-friendly tools and interfaces that allow healthcare organizations to easily implement the proposed approach without requiring extensive technical expertise.
12	EHR	Blockchain		Proxy re-ciphering and traditional methods enhance data security for sensitive information.	Some encryption methods may demand significant computational resources.	Invest in optimizing resource-intensive encryption techniques.
13	EHR	Blockchain	System parameters	The protocol based on keyword search enhances data security and privacy in the EHR system.	Certain encryption techniques may require significant computational resources.	Explore resource-efficient encryption methods.
14	EHR	Blockchain		The paper addresses the issue of cross-blockchain-based EHR storage strategies, which can improve interoperability between healthcare institutions. This ensures that patients can access their EHR data efficiently, even when visiting different hospitals.	Implementing cross-blockchain solutions based on Polka Dot chain technology and RaaS may involve technical complexities. Healthcare organizations may require specialized expertise for deployment.	Implementing cross-blockchain solutions based on Polka Dot chain technology and RaaS may involve technical complexities. Healthcare organizations may require specialized expertise for deployment.
15	EHR	Blockchain		The blockchain-based model proposed in this paper provides patients with control over their health records. Patients can monitor data access and ensure data integrity, enhancing patient empowerment.	Integrating existing healthcare systems with blockchain technology can be challenging, particularly in large, established healthcare organizations.	Designing user-friendly interfaces and tools for patients and healthcare providers can simplify the use of blockchain-based EHR systems.
16	International Classification of Diseases (ICD)	E-Health Care System	Minimum information loss, total number of clusters, cluster centroid	The development of privacy-preserving sub-protocols demonstrates a systematic approach to privacy protection, making it easier to adapt the scheme to various clinical scenarios.	Privacy-preserving protocols, while effective, can be complex to implement and require a thorough understanding of cryptographic techniques. This complexity may hinder adoption by healthcare professionals.	Continued research into optimizing k-anonymization techniques can reduce resource requirements while maintaining strong privacy guarantees.
17	Adult Dataset		Awareness probability	The proposed framework prioritizes patient data privacy by utilizing local differential privacy, ensuring that sensitive patient information remains secure during the collaborative training process.	Implementing a fog-based federated framework with differential privacy can be technically challenging, requiring expertise in both healthcare and privacy-preserving machine learning.	Continuous research and development efforts can focus on optimizing the communication protocols and algorithms used in federated learning to reduce overhead and improve efficiency.
18	Autism-Adolescent Dataset	Health Care	Awareness probability	The blockchain-oriented privacy-preserving EHR sharing protocol ensures that only authorized data requestors can access sensitive EHRs, protecting patient privacy effectively.	The cryptographic operations involved in the protocol may introduce computational overhead, potentially affecting system performance. This complexity can be a limitation in resource-constrained environments.	Researchers can focus on optimizing the cryptographic primitives used in the protocol to reduce computational overhead and enhance efficiency. This can make the solution more practical for real-world implementation.
19	WCE Dataset	IoT-E Health Care	Trajectory	The model employs chaos-based privacy-preserving encryption to protect patients’ privacy effectively. It ensures that sensitive patient images remain confidential and secure.	Implementing advanced encryption techniques can be computationally intensive. This may pose challenges in resource-constrained healthcare environments.	To address computational intensity, research can focus on optimizing encryption algorithms for efficiency, making them more suitable for healthcare applications.
20	Acute Inflammations Dataset, Real Time Dataset	E-Healthcare	Bilinear pairing parameters	The usage of data anonymity enhances privacy by limiting access to factual data.	Implementing data anonymity and semantic strategies may be complex.	Offer training and guidance on the implementation of data anonymity and semantic strategies.
21	PABIDOT Perturbs	Health Care	perturbation parameters	SEAL algorithm ensures data privacy in significant data distribution and evaluation strategies while maintaining efficiency, scalability, and higher attack resistance.	Implementing advanced privacy-preserving algorithms may require expertise and careful configuration.	Developing user-friendly interfaces for complex algorithms can facilitate adoption in healthcare settings.
22	Data Access Control System	Health Care	Security parameter	The fine-grained update strategy ensures data integrity and privacy preservation while minimizing communication and computation costs.	Implementing fine-grained updates may introduce complexity in data management.	Developing clear implementation guidelines can assist healthcare organizations in effectively adopting such strategies.
23	Adult’s Dataset and Bank Marketing Dataset			The clustering-based Privacy Preservation strategy in big data led to successful data reconstruction, ensuring privacy and utility.	Data clustering and generalization may lead to some loss of data granularity.	Research can focus on optimizing data generalization techniques to minimize data loss.
24	Real Time	Smart Home IoT	Independent link padding (ILP) or dependent link padding (DLP), control padding and fragmentation parameters.	privacy risks associated with IoT applications in smart homes, shedding light on potential threats and concerns.	Users may not be able to block outgoing traffic from IoT devices, posing privacy risk	IoT devices should provide users with robust privacy settings to control outgoing traffic effectively.
25	Block Chain	Educational Institution	Local parameters	The system prioritizes privacy by allowing users to have control over their credentials. This is crucial for protecting sensitive student information.	Integrating the blockchain solution with existing education systems and institutions may pose challenges, as standardization and compatibility issues can arise.	Establish interoperability standards to facilitate the integration of blockchain solutions with existing education systems seamlessly.
26	COVID-19 CT Datasets: COVID-19-CTSeg, Mos MedData Dataset	IoMT	Model weights	Collaborative training using data from multiple institutions enhances the robustness and generalizability of deep learning models in medical imaging, making them more effective in diverse clinical settings.	Collaborative learning involves data exchange among institutions, which can result in increased communication overhead and potential latency issues.	Developing efficient communication protocols and data compression techniques can reduce communication overhead and address latency concerns.
27	National Lung Screening Trial, Medical Imaging Data Resource Center, BRATS, and Alzheimer Disease Neuroimaging Initiative,	Medical Diagnosis	Uantitative parameters,	Federated Learning (FL) allows the development of deep learning models across multiple centers without direct data sharing, addressing privacy concerns and legal/ethical issues associated with centralized datasets.	Implementing FL can be complex, requiring coordination among multiple centers and setting up secure communication channels. It may also demand substantial computational resources.	Establishing common data acquisition and reconstruction protocols across centers can mitigate data heterogeneity and enhance model generalization.
28	Real Time	Health Care	Local parameters	FL models can be trained on diverse datasets from different clinical centers, leading to more generalizable models that perform well across a variety of imaging protocols and patient populations.	FL involves iterative communication between centers and a central server, which can lead to increased communication overhead and potentially slower convergence compared to centralized training.	Developing efficient communication protocols and strategies for FL can reduce communication overhead and speed up convergence.
29	Real Time	Health Care		Synthetic datasets aim to mimic real data, ensuring that researchers can still derive valuable insights and conduct meaningful analyses without access to the original, sensitive data.	The synthetic data may not capture fine-grained details present in the original data, potentially limiting certain types of analyses.	Fine-tune synthetic data generation models to specific research objectives and datasets to achieve better mimicry.
30	Real Time	IoHT		Local deep-learning models are trained collaboratively, reducing the need for centralized data collection, which can be time-consuming and resource-intensive.	Implementing and managing a fog-based federated framework can be complex, requiring specialized expertise and infrastructure.	Continuous research and development in privacy-preserving techniques can help bolster security in federated learning, ensuring patient data remains confidential.