Privacy and Security Issues in Cloud Computing: A Survey Paper

Bamasoud, Doaa M.; Al-Dossary, Atheer Salem; Al-Harthy, Nouf Mubarak; Al-Shomrany, Rudaina Abdullah; Alghamdi, Ghaida Saeed; Algahmdi, Rawan Othman

doi:10.1109/icit52682.2021.9491632

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Despite the impressive performance of DL algorithms, numerous recent studies have raised concerns about the security and robustness of machine learning models [3]- [5]. Moreover, the security of such algorithms' execution environments is being questioned [6]- [8]. The realization that DL models are neither safe nor resilient considerably complicates their practical implementation in security-critical applications such as predictive healthcare, which is basically life-critical.…”

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confidence: 99%

The High-Level Practical Overview of Open-Source Privacy-Preserving Machine Learning Solutions

Kuźniewski,

Matusiewicz,

Sapiecha

2022

International Journal of Electronics and Telecommunications

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This paper aims to provide a high-level overview of practical approaches to machine-learning respecting the privacy and confidentiality of customer information, which is called Privacy-Preserving Machine Learning. First, the security approaches in offline-learning privacy methods are assessed. Those focused on modern cryptographic methods, such as Homomorphic Encryption and Secure Multi-Party Computation, as well as on dedicated combined hardware and software platforms like Trusted Execution Environment -Intel ® Software Guard Extensions (Intel ® SGX). Combining the security approaches with different machine learning architectures leads to our Proof of Concept in which the accuracy and speed of the security solutions will be examined. The next step was exploring and comparing the Open-Source Python-based solutions for PPML. Four solutions were selected from almost 40 separate, state-of-the-art systems: SyMPC, TF-Encrypted, TenSEAL, and Gramine. Three different Neural Network architectures were designed to show different libraries' capabilities. The POC solves the image classification problem based on the MNIST dataset. As the computational results show, the accuracy of all considered secure approaches is similar. The maximum difference between non-secure and secure flow does not exceed 1.2%. In terms of secure computations, the most effective Privacy-Preserving Machine Learning library is based on Trusted Execution Environment, followed by Secure Multi-Party Computation and Homomorphic Encryption. However, most of those are at least 1000 times slower than the nonsecure evaluation. Unfortunately, it is not acceptable for a realworld scenario. Future work could combine different security approaches, explore other new and existing state-of-the-art libraries or implement support for hardware-accelerated secure computation.

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confidence: 99%

The High-Level Practical Overview of Open-Source Privacy-Preserving Machine Learning Solutions

Kuźniewski,

Matusiewicz,

Sapiecha

2022

International Journal of Electronics and Telecommunications

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“Comparison of Different Cloud Computing Platforms for Data Analytics”

Gupta,

Sharma

2023

Lecture Notes in Networks and Systems

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Uncovering Cloud Security Complexities-A Comprehensive Five-Perspective Taxonomic Review

Routh,

Ranjan

2024

Preprint

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In the rapidly evolving realm of cloud computing, the surging adoption and its profound role in modern IT infrastructures necessitate a vigilant exploration of emerging security challenges and threats. While prior studies have categorized threats, the need for integrated solutions is evident, and an integrated study is evident, with only a limited few addressing this requirement and providing critical correlations among the factors influencing cloud computing security. This paper conducts a state-of-the-art, in-depth exploration of multifaceted cloud security challenges, organized into a five-perspective taxonomy spanning cloud service models, architecture, networking, stakeholders, and data. It begins by contextualizing the rapid ascent of cloud computing within contemporary IT landscapes. This taxonomy offers a structured framework that dissects challenges and provides a roadmap for problem-solving. Extracting four critical variables from the five perspectives and 8 key metrics for security issues related to cloud service models, this review article bridges the gap between theory and practice, emphasizing the need for precision in navigating these complexities.

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Privacy and Security Issues in Cloud Computing: A Survey Paper

Cited by 3 publications

References 17 publications

The High-Level Practical Overview of Open-Source Privacy-Preserving Machine Learning Solutions

The High-Level Practical Overview of Open-Source Privacy-Preserving Machine Learning Solutions

“Comparison of Different Cloud Computing Platforms for Data Analytics”

Uncovering Cloud Security Complexities-A Comprehensive Five-Perspective Taxonomic Review

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