A Hybrid Deep Learning Model for Privacy-Preserving Service Selection and Fine-Grained Access Control in Cloud Platforms, Privacy Preservation in Cloud Computing

DOI

10.2991/978-94-6239-693-7_88How to use a DOI?

Keywords

Cloud Computing; Secure Service Selection; Access Control; Security Breaches; Deep Learning; Privacy-Preserving; Deep Neural Network; Long Short-Term Memory

Abstract

The amount of heterogeneous services and users deployed on cloud computing systems is soaring at an extremely fast rate and hence, the concern of secure service selection and access control has become a fine-grained access control that is more and more complex. This paper proposes a Hybrid Deep Learning-based Privacy-Preserving Service Selection and Fine-Grained Access Control (HDL-PPSS-FAC) model on cloud platforms in order to address such challenges. The proposed framework involves Deep Neural Network (DNN) to the smart choice of the cloud services based on the condition of the user, the nature of the service, and its reliability, and a Long Short-Term Memory (LSTM) network to arrive at access control decisions. The experimental evaluation conducted on the datasets of cloud services demonstrates that the proposed model can offer up to 18% in terms of improvement in the accuracy of service selection and a decrease in the decision latency compared to the conventional models.

Copyright

© 2026 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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TY  - CONF
AU  - Yamini Anumolu
AU  - Harish Choudary Nannapaneni
AU  - Chandra Shekar Reddy Avula
AU  - Bhaskar Reddy Sareddy
PY  - 2026
DA  - 2026/06/16
TI  - A Hybrid Deep Learning Model for Privacy-Preserving Service Selection and Fine-Grained Access Control in Cloud Platforms, Privacy Preservation in Cloud Computing
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 914
EP  - 922
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_88
DO  - 10.2991/978-94-6239-693-7_88
ID  - Anumolu2026
ER  -