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

Jhansi Bharathi Madavarapu; Giribabu Sinnapolu; Shailaja Salagrama; Prasad Kalapala; K. Reddy Madhavi

doi:10.2991/978-94-6463-471-6_112

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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_112 How 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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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: 10.2991/978-94-6463-471-6_112
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-471-6_112 How to use a DOI?
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

ris enw bib

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  -

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