Hybrid Filtering and Probabilistic Techniques for Privacy-Preserving Community Detection in OSNs
Journal Title: International Journal of Experimental Research and Review - Year 2024, Vol 41, Issue 5
Abstract
Online Social Networks (OSNs) face the major challenge of protecting participant's privacy, due to the high dimensionality and volume of the data. In real-time social networks, where hundreds of personal details of people are shared every day, there remains a significant threat to privacy. Privacy preservation is challenging for a community detection problem due to the high computational complexity and memory requirements, especially in larger real-world OSN graphs. Although weighted nodes provide better results, as they allow capturing the frequencies of the values, the privacy preservation of sensitive attributes such as specific profiles becomes harder compared to these models. This problem can result in queries and subsequent learnings from social network profiles of specific individuals, which may be of personal, political or otherwise concern. Online social networks (OSNs) grapple with significant privacy challenges due to the extensive dimensions and vast quantities of data involved. This research fills the void in current privacy-preserving community detection methodologies, which face problems in computational complexity and memory usage in large-scale OSNs. The proposed framework seeks to bolster privacy preservation through a comprehensive multi-step process. Data filtering uses a blended data filter system to remove outliers and irrelevant data, thus enhancing the quality of the input data. Density-Oriented Clustering phase employs a density-oriented clustering model to identify communities, with each cluster representing a distinct community. Privacy Preservation component introduces a new privacy preservation technique for sensitive OSN attributes, surpassing existing k-anonymization methods. The developed density-based social network community detection model and its novel privacy-preserving scheme are evaluated using the datasets Yelp, Football, Zachary and Dolphin from the SNAP dataset. Experimental results on these datasets embed a comprehensive evaluation based on the order of each node and the graph-based networks, where each node is laden with weights as proximity values, indicating the semantic proximity between communities and individuals. The proposed framework employs the normalized mutual information (NMI), modularity (Q), Rand index and runtime measurements to demonstrate widespread advantages in the multi-dimensional functional space, including greater accuracy, cluster compatibility, and computational tractability over the existing prominent traditional models for OSNs.
Authors and Affiliations
Shamila M, G. Rekha, K. Vinuthna Reddy
Keywords
Related Articles
- EP ID EP741638
- DOI 10.52756/ijerr.2024.v41spl.015
- Views 21
- Downloads 0
Impact of backwardness on health-case study Pakhiralaya village, Gosaba Block, Sundarban, West Bengal, India
The Sundarban is a national park, biosphere reserve and tiger reserve in West Bengal, India. Pakhiralaya village is a part of Sundarban and it located exact opposite to the buffer area of Sundarban reserve forest. This v...
Fabrication of microspheres and characterization of antimicrobial and anti-inflammatory activity isolated fraction from total alcoholic extract of Cassia Fistula (Linn.) in carrageenan-Type-IV induced inflammatory rats
The Cassia fistula Linn is known as the golden shower tree. It’s also referred to as a "disease killer" in Ayurvedic medicine. The current study investigates the effect and estimate of antimicrobial and Anti-inflammatory...
Phytochemical Investigation and Antiulcer Potential of Strychnos Nux vomica Seed Extract in Adult Wistar Rats
The current work sought to assess the possible anti-ulcer efficacy of Strychnos nux-vomica methanolic seed extracts in rats by conducting a thorough phytochemical analysis. This study aims to indicate and discover benefi...
Antidiabetic and antihyperlipidemic effects of crude fractions from Chlorophytum borivilianum root methanolic extract on streptozotocin induced diabetic rats and phytochemical investigation by LCMS analysis.
To evaluate the in vitro and in vivo pharmacological efficacy of the plant Chlorophytum borivilianum in diabetes and hyperlipidemia and to confine and describe the synthetic constituents from the roots that are in charge...
A Hybrid Framework for Plant Leaf Region Segmentation: Comparative Analysis of Swarm Intelligence with Convolutional Neural Networks
Agriculture is important for the survival of humanity since about 70% of the world's population is engaged in agricultural pursuits to varying degrees. The previous and present methodology lacks ways to identify diseases...