Comprehensive Privacy Risk Assessment in Social Networks Using User Attributes Social Graphs and Text Analysis
Md Jahangir Alam, Ismail Hossain, Sai Puppala, Sajedul Talukder

TL;DR
This paper introduces a comprehensive framework for assessing privacy risks in social networks by integrating user attributes, social graph structures, and content analysis, validated on real datasets and user studies.
Contribution
It presents the CPRS framework that quantifies privacy risks holistically, combining multiple data dimensions into a unified score, a novel approach in privacy risk assessment.
Findings
Average risk score is 0.478, increasing to 0.501 in graph-sensitive scenarios.
Graph-based risks are higher than content and attribute risks.
User study shows 85% find the privacy dashboard clear and useful.
Abstract
The rise of social networking platforms has amplified privacy threats as users increasingly share sensitive information across profiles, content, and social connections. We present a Comprehensive Privacy Risk Scoring (CPRS) framework that quantifies privacy risk by integrating user attributes, social graph structures, and user-generated content. Our framework computes risk scores across these dimensions using sensitivity, visibility, structural similarity, and entity-level analysis, then aggregates them into a unified risk score. We validate CPRS on two real-world datasets: the SNAP Facebook Ego Network (4,039 users) and the Koo microblogging dataset (1M posts, 1M comments). The average CPRS is 0.478 with equal weighting, rising to 0.501 in graph-sensitive scenarios. Component-wise, graph-based risks (mean 0.52) surpass content (0.48) and profile attributes (0.45). High-risk attributes…
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