CNN based Blockchain Information Protection Model for Emerging Cloud Applications

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Published: Sep 21, 2023
DOI: https://doi.org/10.17762/ijritcc.v12i1.7913
Keywords:
Blockchain, Data Privacy, Convolutional Neural Networks, Cloud Environment, Homomorphic Encryption

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Chaitanya Kulkarni
Computer Engineering Department, VPKBIET, Baramati, India
Kamlesh Vasantrao Patil
Department of Information Technology, Bharati Vidyapeeth's College of Engineering for Women Pune, India
Mugdha Arvind Rane
Department of Information Technology, Bharati Vidyapeeth's College of Engineering for Women Pune, India
Ashwini Vitthal Kanade
Department of Information Technology, Bharati Vidyapeeth's College of Engineering for Women Pune, India
Kavita Shantanu Sawant
Computer Engineering Department, Bharati Vidyapeeth's College of Engineering for Women Pune, India

Abstract

In the age of mobile internet, the amount of data is growing, and ability to process service data is always getting better. So, protecting data privacy and making sure the service environment is trustworthy have become very important. This paper looks at a trusted privacy service computing model for common uses of convolutional neural networks. The goal is to find data and model calculation methods that support homomorphic encryption to protect data privacy. Build a service process certificate and a method for distributing calculation rights based on blockchain and new contract technology to make sure that service calculations are open, trustworthy, and easy to track. Explore how the new cloud environment resource data service model helps resource providers, model owners, and users work together to make the most of their resources and grow the sharing economy. Lastly, experiments are done to figure out how the model protects privacy.

Article Details

How to Cite
Kulkarni, C. ., Patil, K. V. ., Rane, M. A. ., Kanade, A. V. ., & Sawant, K. S. . (2023). CNN based Blockchain Information Protection Model for Emerging Cloud Applications. International Journal on Recent and Innovation Trends in Computing and Communication, 12(1), 77–84. https://doi.org/10.17762/ijritcc.v12i1.7913
Issue
Vol. 12 No. 1 (2024)
Section
Articles

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