A novel secure and distributed architecture for privacy-preserving healthcare system

Haque, Rakib Ul; Hasan, A S M Touhidul; Daria, Apubra; Rasool, Abdur; Chen, Hui; Jiang, Qingshan; Zhang, Yuqing

doi:10.1016/j.jnca.2023.103696

Cited by 8 publications

(1 citation statement)

References 25 publications

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“…Paper [11] proposes a secure and distributed architecture for privacy-preserving healthcare systems. This architecture consists of a custodian, a hospital, and a third-party service provider.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Cloud Data Privacy with a Scalable Hybrid Approach: HE-DP-SMC

Jaibir Singh

2024

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Cloud computing has become a popular way to store and access data. However, there are concerns about the privacy of data stored in the cloud. This paper proposes a novel privacy-preserving mechanism that uses a combination of homomorphic encryption, differential privacy, and secure multi-party computation. The mechanism allows users to store their data in the cloud while still ensuring the privacy of their data. The mechanism combines three cryptographic techniques: homomorphic encryption(HE), differential privacy(DP), and secure multi-party computation(SMC). Homomorphic encryption enables computations on encrypted data, ensuring that confidentiality is maintained throughout the storage process. Differential privacy(DP) techniques add an additional layer of protection by injecting controlled noise into query responses, preserving the privacy of individual data items. Secure multi-party computation protocols ensure that computations involving multiple cloud servers are performed securely without compromising the confidentiality of data. The mechanism is efficient and scalable. It can be used to protect the privacy of data in a wide range of applications, including healthcare, finance, and government. The quantitative results obtained from the performance evaluation of the proposed privacy-preserving mechanism reveal valuable insights into its efficiency and effectiveness. The mechanism shows promising results for small to medium-sized datasets. As the dataset size increases, the execution time, communication overhead, and storage requirements also increase proportionally. However, the mechanism maintains its scalability and efficiency. In comparison with baseline systems, the proposed mechanism outperforms systems without privacy-preserving mechanisms and only exhibits slightly slower performance than systems with traditional encryption. The proposed mechanism is a promising new approach to privacy-preserving cloud computing. It provides a strong level of protection against a variety of attacks, while still being efficient and scalable. It has the potential to revolutionize the way we store and share sensitive data..

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Section: Related Workmentioning

confidence: 99%