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An Efficient and Secure Data Sharing Method Using Asymmetric Pairing with Shorter Ciphertext to Enable Rapid Learning in Healthcare.
Yadav, Snehlata; Tiwari, Namita.
  • Yadav S; Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal 462003, India.
  • Tiwari N; Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal 462003, India.
Comput Intell Neurosci ; 2022: 4788031, 2022.
Article in English | MEDLINE | ID: covidwho-1799194
ABSTRACT
The recent advent of cloud computing provides a flexible way to effectively share data among multiple users. Cloud computing and cryptographic primitives are changing the way of healthcare unprecedentedly by providing real-time data sharing cost-effectively. Sharing various data items from different users to multiple sets of legitimate subscribers in the cloud environment is a challenging issue. The online electronic healthcare system requires multiple data items to be shared by different users for various purposes. In the present scenario, COVID-19 data is sensitive and must be encrypted to ensure data privacy. Secure sharing of such information is crucial. The standard broadcast encryption system is inefficient for this purpose. Multichannel broadcast encryption is a mechanism that enables secure sharing of different messages to different set of users efficiently. We propose an efficient and secure data sharing method with shorter ciphertext in public key setting using asymmetric (Type-III) pairings. The Type-III setting is the most efficient form among all pairing types regarding operations required and security. The semantic security of this method is proven under decisional BDHE complexity assumption without random oracle model.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Security / COVID-19 Type of study: Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Comput Intell Neurosci Journal subject: Medical Informatics / Neurology Year: 2022 Document Type: Article Affiliation country: 2022

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Security / COVID-19 Type of study: Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Comput Intell Neurosci Journal subject: Medical Informatics / Neurology Year: 2022 Document Type: Article Affiliation country: 2022