An attack Resistant Privacy-Preserving Access Control Scheme for Outsourced E-pharma Data in Cloud

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Published Oct 31, 2022
https://doi.org/10.47164/ijngc.v13i3.859
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Volume 13, Special Issue 3, October 2022

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Mageto Stephen N
Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, India
Dr N V Balaji

Abstract

Key Aggregate Cryptosystem was used to construct a safe and scalable healthcare data exchange system in the cloud (KAC). KAC offered single-key encryption and decryption rights delegation for any group of ciphertexts utilizing an aggregated secret key. However, leakage of delegation keys and a specified bound on number of maximal ciphertext classes were major issues with KAC.The Scalable and Strengthened -KAC (SSKAC) approach was created to decide the number of ciphertext classes using an elliptic curve dynamically while simultaneously boosting the security level of KAC by utilising double encryption. Access control is crucial because the majority of healthcare data is extremely sensitive and should only be accessed by those who are permitted. As a result, SSKAC includes attribute-based access control with anonymous authentication to give access control on health data with anonymous authentication. However, the access policy may expose end-user privacy to an attacker. To tackle this problem,we used the cuckoo filter to disguise the right of entry policy tosafeguard the privacy information of the owner. The inference assault affects medical records. One of the strategies for ensuring data confidentiality from unforeseen data access is inference control. The Individual data privacy is secured through the use of two inference control methods; The Query Set Size Prevention and K-Anonymity.

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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Stephen N, M., & Balaji, D. N. V. . (2022). An attack Resistant Privacy-Preserving Access Control Scheme for Outsourced E-pharma Data in Cloud . International Journal of Next-Generation Computing, 13(3). https://doi.org/10.47164/ijngc.v13i3.859
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