Design an attribute based health record protection algorithm for healthcare services in cloud environment

Mubarakali, Azath; Ashwin, M.; Mavaluru, Dinesh; Kumar, A. Dinesh

doi:10.1007/s11042-019-7494-7

Cited by 28 publications

(13 citation statements)

References 10 publications

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“…Azath et al proposed Attribute based health record protection scheme for securing the data is established to reduce the administrational and computational load on health care system. The proposed scheme involved access control and privilege mode to encrypt the information and authenticate the message respectively to preserve the data [22]. Kashish et al proposed biometric authentication approach for securing the cloud health data was proposed through the neural network and named it as BAM HealthCloud.…”

Section: Related Workmentioning

confidence: 99%

TAR-AFT: A Framework to Secure Shared Cloud Data with Group Management

Ambika¹,

Moses²

2022

Intelligent Automation &Amp; Soft Computing

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In addition to replacing desktop-based methods, cloud computing is playing a significant role in several areas of data management. The health care industry, where so much data is needed to be handled correctly, is another arena in which artificial intelligence has a big role to play. The upshot of this innovation led to the creation of multiple healthcare clouds. The challenge of data privacy and confidentiality is the same for different clouds. Many existing works has provided security framework to ensure the security of data in clouds but still the drawback on revocation, resisting collusion attack along with privacy of data present a complex problem. For preserving the data privacy and confidentiality, a novel framework is proposed with two novel algorithms of Threat Aware Revocation (TAR) and Advance Flexi Twister Secret Block Encryption Standard (AFT-SBES) both are named as TAR-AFT. The TAR algorithm is mainly focus on generates the user signature and the AFT-SBES algorithm to generate the key. Both the signature and key are distributed to the users for enhancing the security to access cloud storage and files. The self-session shadow approach is used to monitor the activities of the cloud users. The revocation is carried out through removal of signature from the Enhanced Dynamic Hash Table (EDHT). From the performance analysis of TAR-AFT, it provides more effective to accessing of data stored in cloud and security such as data privacy and confidentiality.

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Section: Related Workmentioning

confidence: 99%

TAR-AFT: A Framework to Secure Shared Cloud Data with Group Management

Ambika¹,

Moses²

2022

Intelligent Automation &Amp; Soft Computing

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“…The mathematical model is used to assess the transmission risk of COVID-19 in various facilities. The agent-based simulation model which is used to take better decision for prevention of COVID-19 [22,23]. The synergic deep model is used to learn and predict various metrics like duration of days, discharge disposition, and inpatient expense for total hip arthroplasty [24,25].…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 Transmission Risks Assessment using Agent-Based Weighted Clustering Approach

Sagar¹,

Pavan²,

Krishna³

et al. 2020

IJACSA

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Coronavirus is a pandemic disease spreading from human-to-human rapidly all over the world. This virus is origin from common cold to severe disease such as MERS-CoV and SARS-CoV. Initially it was identified in China, December 2019. The main aim of this research is used to identify the COVID-19 transmission risks assessment from human-to-human within a cluster. The agent-based weighted clustering approach is used to identify the corona virus infected people rapidly within a cluster. In the weighted clustered approach, the normal agents are consisted as susceptible node and the corona virus infected people are considered as malicious node. The Cluster Head (CH) is elected based upon some weighting factors and the trust value is evaluated for all the agents within the cluster. The cluster head were periodically transfers the malicious node information to all other nodes within the cluster. Finally, the agent-based weighted clustering machine learning model approach is used to identify the number of corona virus infected people within the cluster.

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“…ABE has also been proposed for the eHealth domain [7,13,12,6,20,17,5]. Greene et al [7] use KP-ABE to restrict access to health data, and additionally incorporate hash-chaining for time-based access control; however the scenario is different from ours and deals with data sharing from smart health devices to a cloud database.…”

Section: Related Workmentioning

confidence: 99%

Health Access Broker: Secure, Patient-Controlled Management of Personal Health Records in the Cloud

Abaid¹,

Shaghaghi²,

Gunawardena³

et al. 2020

Preprint

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Secure and privacy-preserving management of Personal Health Records (PHRs) has proved to be a major challenge in modern healthcare. Current solutions generally do not offer patients a choice in where the data is actually stored, and also rely on at least one fully trusted element that patients must also trust with their data. In this work, we present the Health Access Broker (HAB), a patient-controlled service for secure PHR sharing that (a) does not impose a specific storage location (uniquely for a PHR system), and (b) does not assume any of its components to be fully secure against adversarial threats. Instead, HAB introduces a novel auditing and intrusion-detection mechanism where its workflow is securely logged and continuously inspected to provide auditability of data access and quickly detect any intrusions.

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Design an attribute based health record protection algorithm for healthcare services in cloud environment

Cited by 28 publications

References 10 publications

TAR-AFT: A Framework to Secure Shared Cloud Data with Group Management

TAR-AFT: A Framework to Secure Shared Cloud Data with Group Management

COVID-19 Transmission Risks Assessment using Agent-Based Weighted Clustering Approach

Health Access Broker: Secure, Patient-Controlled Management of Personal Health Records in the Cloud

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