Secure and privacy-preserving sharing of personal health records with multi-party pre-authorization verification

Tan, K. J.; Chi, Chi‐Hung; Lam, Kwok‐Yan

doi:10.1007/s11276-022-03114-6

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…In general, such techniques requires the dataset to be encrypted before storage and any queries are performed on the encrypted dataset. Such approaches may use various cryptographic tools such as symmetric searchable encryption [22,23], attribute-based encryption [24,25], identity-based encryption [26,27], federated learning [28][29][30], or their combinations [31][32][33]. Note that in such approaches, each data is encrypted or processed as a whole.…”

Section: Related Workmentioning

confidence: 99%

Decentralized Data Sharing Using Searchable Encryption Database with Selective Data Retrieval

Gunawan,

Tjuawinata,

Lam

et al. 2023

2023 10th International Conference on ICT for Smart Society (ICISS)

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Secure data sharing platform is an essential component in various elds, including smart cities, AI technologies as well as big data analysis where sensitive data may be owned by a large amount of entities with their own respective privacy concerns. In addition to the privacy issue, data sharing scheme needs to also preserve the data owner's sovereignty where the data owner should always have the ability to decide how the data can be shared. Moreover, in most cases, data sharing may not require the whole dataset to be shared to the requester, which implies that selective retrieval of parts of the data to be shared should also be a feature that can benet the scheme. In this work, we propose a data sharing infrastructure and a scheme following such infrastructure which is constructed based on the recently proposed MongoDB Queryable encryption. In our scheme, we show that privacy and sovereignty can be guaranteed, which addresses all the concerns that we have previously identied.

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Section: Related Workmentioning

confidence: 99%

Decentralized Data Sharing Using Searchable Encryption Database with Selective Data Retrieval

Gunawan,

Tjuawinata,

Lam

et al. 2023

2023 10th International Conference on ICT for Smart Society (ICISS)

Self Cite

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A Deep-Learning Neural Network Approach for Secure Wireless Communication in the Surveillance of Electronic Health Records

Diao

Sun

2023

Processes

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The electronic health record (EHR) surveillance process relies on wireless security administered in application technology, such as the Internet of Things (IoT). Automated supervision with cutting-edge data analysis methods may be a viable strategy to enhance treatment in light of the increasing accessibility of medical narratives in the electronic health record. EHR analysis structured data structure code was used to obtain data on initial fatality risk, infection rate, and hazard ratio of death from EHRs for prediction of unexpected deaths. Patients utilizing EHRs in general must keep in mind the significance of security. With the rise of the IoT and sensor-based Healthcare 4.0, cyber-resilience has emerged as a need for the safekeeping of patient information across all connected devices. Security for access, amendment, and storage is cumulatively managed using the common paradigm. For improving the security of surveillance in the aforementioned services, this article introduces an endorsed joint security scheme (EJSS). This scheme recognizes the EHR utilization based on the aforementioned processes. For each process, different security measures are administered for sustainable security. Access control and storage modification require relative security administered using mutual key sharing between the accessing user and the EHR database. In this process, the learning identifies the variations in different processes for reducing adversarial interruption. The federated learning paradigm employed in this scheme identifies concurrent adversaries in the different processes initiated at the same time. Differentiating the adversaries under each process strengthens mutual authentication using individual attributes. Therefore, individual surveillance efficiency through log inspection and adversary detection is improved for heterogeneous and large-scale EHR databases.

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Secure and privacy-preserving sharing of personal health records with multi-party pre-authorization verification

Cited by 2 publications

References 52 publications

Decentralized Data Sharing Using Searchable Encryption Database with Selective Data Retrieval

Decentralized Data Sharing Using Searchable Encryption Database with Selective Data Retrieval

A Deep-Learning Neural Network Approach for Secure Wireless Communication in the Surveillance of Electronic Health Records

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