A Privacy-Enhanced Framework for Securing User Data on Cloud-Based Social Networks

Online Social Networks (OSNs) have become integral to modern life, enabling people to communicate, share information, and stay connected over vast distances. However, the rising use of OSNs has sparked significant concerns regarding the privacy and security of user data. This paper presents an innovative method for strengthening data privacy and security in cloud-enabled OSNs utilizing an E-ABE system. The proposed solution employs flow graph analysis to verify relationships, ensuring secure user data exchange while offering fine-grained access control. This approach addresses the limitations of existing methods, which often fail to provide comprehensive privacy and security measures. By leveraging cryptographic techniques and secure communication protocols, the E-ABE model allows for the controlled sharing of sensitive information, ensuring that only authorized users can access data. The system's design includes roles for Cloud Service Providers (CSPs), Trusted Attribute Authorities (TAAs), and end-users (followers and followees), each contributing to the overall security framework. Experimental results demonstrate the effectiveness of the proposed method in reducing key generation time, data encryption and decryption time, and communication costs while maintaining high levels of authorization accuracy. This research contributes to the field by providing a robust solution for protecting user data in cloud-based OSNs, highlighting its potential for broader application in other domains requiring stringent data privacy and security measures.

Authors

Publication Details

Journal
International Journal of Electrical and Electronics Engineering
Published
2024-12-31
DOI
https://doi.org/10.14445/23488379/ijeee-v11i12p110
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Privacy-Enhanced Framework for Securing User Data on Cloud-Based Social Networks

Gangadhara Rao Kancherla, Nagamuthu Krishnan S S
International Journal of Electrical and Electronics Engineering
Privacy-Preserving Technologies in Data
article

A Privacy-Enhanced Framework for Securing User Data on Cloud-Based Social Networks

Gangadhara Rao Kancherla, Nagamuthu Krishnan S S
article en

Abstract

Online Social Networks (OSNs) have become integral to modern life, enabling people to communicate, share information, and stay connected over vast distances. However, the rising use of OSNs has sparked significant concerns regarding the privacy and security of user data. This paper presents an innovative method for strengthening data privacy and security in cloud-enabled OSNs utilizing an E-ABE system. The proposed solution employs flow graph analysis to verify relationships, ensuring secure user data exchange while offering fine-grained access control. This approach addresses the limitations of existing methods, which often fail to provide comprehensive privacy and security measures. By leveraging cryptographic techniques and secure communication protocols, the E-ABE model allows for the controlled sharing of sensitive information, ensuring that only authorized users can access data. The system's design includes roles for Cloud Service Providers (CSPs), Trusted Attribute Authorities (TAAs), and end-users (followers and followees), each contributing to the overall security framework. Experimental results demonstrate the effectiveness of the proposed method in reducing key generation time, data encryption and decryption time, and communication costs while maintaining high levels of authorization accuracy. This research contributes to the field by providing a robust solution for protecting user data in cloud-based OSNs, highlighting its potential for broader application in other domains requiring stringent data privacy and security measures.

International Journal of Electrical and Electronics EngineeringVol. 11(12)
Openalex Percentile: Top 77%
Privacy-Preserving Technologies in Data
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Privacy-Enhanced Framework for Securing User Data on Cloud-Based Social Networks — Gangadhara Rao Kancherla, Nagamuthu Krishnan S S · International Journal of Electrical and Electronics Engineering (2024) | TGRS Research Map | TGRS