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Cloud Computing: Privacy Protection and Data Security
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Cloud Computing: Privacy Protection and Data Security

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 9845

Special Issue Editors

Prof. Dr. Liangyin Chen

E-Mail Website
Guest Editor
School of Computer Science, Sichuan University, Chengdu 610065, China
Interests: wireless sensor networks; intelligent internet of things and IoT security; industrial internet; blockchains; big data
Prof. Dr. Pengpeng Chen

E-Mail Website
Guest Editor
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
Interests: network security; privacy protection
Dr. Yanru Chen

E-Mail Website
Co-Guest Editor
School of Computer Science, Sichuan University, Chengdu 610017, China
Interests: industrial control system security; privacy protection; authentication key negotiation protocols; intrusion detection; intelligent internet of things; data intelligence in industrial internet; blockchain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of rapid technological advancement, cloud computing has emerged as a cornerstone of modern IT infrastructures, enabling seamless data storage, processing, and accessibility. However, the increasing reliance on cloud solutions has brought forth critical concerns regarding data security and privacy. This Special Issue is dedicated to addressing these challenges head-on by presenting state-of-the-art research, insights, and innovative solutions that aim to fortify data security and uphold user privacy in cloud computing environments. We invite contributions from researchers, practitioners, and experts in the field to share their knowledge and expertise on safeguarding sensitive information in the cloud. Join us in delving into the myriad dimensions of security and privacy within the expansive realm of cloud computing.

Prof. Dr. Liangyin Chen
Prof. Dr. Pengpeng Chen
Guest Editors

Dr. Yanru Chen
Co-Guest Editor

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Keywords

  • cloud computing
  • data security
  • privacy protection
  • encryption techniques
  • access control
  • secure data sharing
  • regulatory compliance
  • threat detection
  • authenticated key agreement protocol
  • physical layer authentication
  • attack detection
  • false data injection attack
  • industrial control system
  • data sharing

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Published Papers (4 papers)

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Research

25 pages, 636 KiB  
Article
A User-Centered Framework for Data Privacy Protection Using Large Language Models and Attention Mechanisms
by Shutian Zhou, Zizhe Zhou, Chenxi Wang, Yuzhe Liang, Liangyu Wang, Jiahe Zhang, Jinming Zhang and Chunli Lv
Appl. Sci. 2024, 14(15), 6824; https://doi.org/10.3390/app14156824 - 5 Aug 2024
Cited by 1 | Viewed by 905
Abstract
This paper introduces a user-centered data privacy protection framework utilizing large language models (LLMs) and user attention mechanisms, which are tailored to address urgent privacy concerns in sensitive data processing domains like financial computing and facial recognition. The innovation lies in a novel [...] Read more.
This paper introduces a user-centered data privacy protection framework utilizing large language models (LLMs) and user attention mechanisms, which are tailored to address urgent privacy concerns in sensitive data processing domains like financial computing and facial recognition. The innovation lies in a novel user attention mechanism that dynamically adjusts attention weights based on data characteristics and user privacy needs, enhancing the ability to identify and protect sensitive information effectively. Significant methodological advancements differentiate our approach from existing techniques by incorporating user-specific attention into traditional LLMs, ensuring both data accuracy and privacy. We succinctly highlight the enhanced performance of this framework through a selective presentation of experimental results across various applications. Notably, in computer vision, the application of our user attention mechanism led to improved metrics over traditional multi-head and self-attention methods: FasterRCNN models achieved precision, recall, and accuracy rates of 0.82, 0.79, and 0.80, respectively. Similar enhancements were observed with SSD, YOLO, and EfficientDet models with notable increases in all performance metrics. In natural language processing tasks, our framework significantly boosted the performance of models like Transformer, BERT, CLIP, BLIP, and BLIP2, demonstrating the framework’s adaptability and effectiveness. These streamlined results underscore the practical impact and the technological advancement of our proposed framework, confirming its superiority in enhancing privacy protection without compromising on data processing efficacy. Full article
(This article belongs to the Special Issue Cloud Computing: Privacy Protection and Data Security)
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20 pages, 1461 KiB  
Article
AHAC: Advanced Network-Hiding Access Control Framework
by Mudi Xu, Benfeng Chen, Zhizhong Tan, Shan Chen, Lei Wang, Yan Liu, Tai Io San, Sou Wang Fong, Wenyong Wang and Jing Feng
Appl. Sci. 2024, 14(13), 5593; https://doi.org/10.3390/app14135593 - 27 Jun 2024
Viewed by 780
Abstract
In the current context of rapid Internet of Things (IoT) and cloud computing technology development, the Single Packet Authorization (SPA) protocol faces increasing challenges, such as security threats from Distributed Denial of Service (DDoS) attacks. To address these issues, we propose the Advanced [...] Read more.
In the current context of rapid Internet of Things (IoT) and cloud computing technology development, the Single Packet Authorization (SPA) protocol faces increasing challenges, such as security threats from Distributed Denial of Service (DDoS) attacks. To address these issues, we propose the Advanced Network-Hiding Access Control (AHAC) framework, designed to enhance security by reducing network environment exposure and providing secure access methods. AHAC introduces an independent control surface as the access proxy service and combines it with a noise generation mechanism for encrypted access schemes, replacing the traditional RSA signature method used in SPA protocols. This framework significantly improves system security, reduces computational costs, and enhances key verification efficiency. The AHAC framework addresses several limitations inherent in SPA: users need to know the IP address of resources in advance, exposing the resource address to potential attacks; SPA’s one-way authentication mechanism is insufficient for multi-level authentication in dynamic environments; deploying the knocking module and protected resources on the same host can lead to resource exhaustion and service unavailability under heavy loads; and SPA often uses high-overhead encryption algorithms like RSA2048. To counter these limitations, AHAC separates the Port Knocking module from the access control module, supports mutual authentication, and implements an extensible two-way communication mechanism. It also employs ECC and ECDH algorithms, enhancing security while reducing computational costs. We conducted extensive experiments to validate AHAC’s performance, high availability, extensibility, and compatibility. The experiments compared AHAC with traditional SPA in terms of time cost and performance. Full article
(This article belongs to the Special Issue Cloud Computing: Privacy Protection and Data Security)
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22 pages, 522 KiB  
Article
Exploiting Hidden Information Leakages in Backward Privacy for Dynamic Searchable Symmetric Encryption
by Hyundo Yoon, Muncheon Yu, Changhee Hahn, Dongyoung Koo and Junbeom Hur
Appl. Sci. 2024, 14(6), 2287; https://doi.org/10.3390/app14062287 - 8 Mar 2024
Viewed by 727
Abstract
Dynamic searchable symmetric encryption (DSSE) enables searches over encrypted data as well as data dynamics such as flexible data addition and deletion operations. A major security concern in DSSE is how to preserve forward and backward privacy, which are typically achieved by removing [...] Read more.
Dynamic searchable symmetric encryption (DSSE) enables searches over encrypted data as well as data dynamics such as flexible data addition and deletion operations. A major security concern in DSSE is how to preserve forward and backward privacy, which are typically achieved by removing the linkability between the newly added data and previous queries, and between the deleted data and future queries, respectively. After information leakage types were formally defined for different levels of backward privacy (i.e., Type-I, II, III), many backward private DSSE schemes have been constructed under the definitions. However, we observed that the backward privacy can be violated by leveraging additional secondary leakage, which is typically leaked in specific constructions of schemes in spite of their theoretical guarantees. In this paper, in order to understand the security gap between the theoretical definitions and practical constructions, we conduct an in-depth analysis of the root cause for the secondary leakage, and demonstrate how it can be abused to violate Type-II backward privacy (e.g., the exposure of the deletion history) of DSSE constructions in practice. We then propose a novel Type-II backward private DSSE scheme based on Intel SGX, which is resilient to the secondary leakage abuse attack. According to the comparative analysis of our scheme with the state-of-the-art SGX-based DSSE schemes, Bunker-B (EuroSec’19) and SGX-SE1 (ACNS’20), our scheme shows higher efficiency in terms of the search latency with a negligible utility loss under the same security level (cf. Bunker-B) while showing similar efficiency with a higher security level (cf. SGX-SE1). Finally, we formally prove that our scheme guarantees Type-II backward privacy. Full article
(This article belongs to the Special Issue Cloud Computing: Privacy Protection and Data Security)
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24 pages, 4995 KiB  
Article
Strengthening Cloud Security: An Innovative Multi-Factor Multi-Layer Authentication Framework for Cloud User Authentication
by Ayman Mohamed Mostafa, Mohamed Ezz, Murtada K. Elbashir, Meshrif Alruily, Eslam Hamouda, Mohamed Alsarhani and Wael Said
Appl. Sci. 2023, 13(19), 10871; https://doi.org/10.3390/app131910871 - 30 Sep 2023
Cited by 12 | Viewed by 6004
Abstract
Cloud multi-factor authentication is a critical security measure that helps strengthen cloud security from unauthorized access and data breaches. Multi-factor authentication verifies that authentic cloud users are only authorized to access cloud apps, data, services, and resources, making it more secure for enterprises [...] Read more.
Cloud multi-factor authentication is a critical security measure that helps strengthen cloud security from unauthorized access and data breaches. Multi-factor authentication verifies that authentic cloud users are only authorized to access cloud apps, data, services, and resources, making it more secure for enterprises and less inconvenient for users. The number of authentication factors varies based on the security framework’s architecture and the required security level. Therefore, implementing a secured multi-factor authentication framework in a cloud platform is a challenging process. In this paper, we developed an adaptive multi-factor multi-layer authentication framework that embeds an access control and intrusion detection mechanisms with an automated selection of authentication methods. The core objective is to enhance a secured cloud platform with low false positive alarms that makes it more difficult for intruders to access the cloud system. To enhance the authentication mechanism and reduce false alarms, multiple authentication factors that include the length, validity, and value of the user factor is implemented with a user’s geolocation and user’s browser confirmation method that increase the identity verification of cloud users. An additional AES-based encryption component is applied to data, which are protected from being disclosed. The AES encryption mechanism is implemented to conceal the login information on the directory provider of the cloud. The proposed framework demonstrated excellent performance in identifying potentially malicious users and intruders, thereby effectively preventing any intentional attacks on the cloud services and data. Full article
(This article belongs to the Special Issue Cloud Computing: Privacy Protection and Data Security)
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