Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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A Managed Access System Provider for Safe and Confirmable Fog-Cloud Computing

Authors
Jhansi Bharathi Madavarapu1, *, Giribabu Sinnapolu2, Shailaja Salagrama3, Prasad Kalapala4, K. Reddy Madhavi5
1Department of Information Technology, University of the Cumberland’s, Williamsburg, KY, USA, 40769
2Department of Electrical Engineering, Oakland University, Rochester, MI, 48309, USA
3Department of Information Technology, University of the Cumberland’s, Williamsburg, KY, USA, 40769
4Department of Mechanical Engineering, JNTUK Kakinada, Andhra Pradesh, India
5Professor, AI&ML, School of Computing, Mohan Babu University, Tirupati, India
*Corresponding author. Email: jhansimadavarapu@gmail.com
Corresponding Author
Jhansi Bharathi Madavarapu
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_112How to use a DOI?
Keywords
Cloud Computing; Security; Internet of Things” (IoT); Encryption; and Access Control
Abstract

This research introduces HPCS, a model for a clinical decision support system that makes use of private and public cloud computing. In order to reliably and safely track a patient's vitals, a fog server inside the HPCS architecture uses a basic data mining method. It is possible to securely send aberrant symptom reports to a cloud server for accurate prediction the moment they are detected. To safely build a one-layer neural network on fog servers, we present a novel and secure outsourced inner-product protocol. An approach to piecewise polynomial computing allows for the implementation of any activation function in a multilayer neural network on a cloud server while ensuring user privacy is preserved. The problem of computational overload is the focus of our novel protocol, the “privacy-preserving fraction approximation protocol.“ We show in simulations that HPCS satisfies the goal of health monitoring without exposing patients’ privacy to third parties by striking the ideal balance between real-time processing and very precise prediction.

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Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_112How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Jhansi Bharathi Madavarapu
AU  - Giribabu Sinnapolu
AU  - Shailaja Salagrama
AU  - Prasad Kalapala
AU  - K. Reddy Madhavi
PY  - 2024
DA  - 2024/07/30
TI  - A Managed Access System Provider for Safe and Confirmable Fog-Cloud Computing
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1173
EP  - 1181
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_112
DO  - 10.2991/978-94-6463-471-6_112
ID  - Madavarapu2024
ER  -