Comprehensive Privacy Risk Assessment in Social Networks Using User Attributes Social Graphs and Text Analysis

2025-07-20

AI Summary

Quantifying privacy risks in social networks is challenging due to the coexistence of heterogeneous, multi-source data—user attributes, social graph structures, and user-generated content (UGC). Method: This paper proposes the first unified privacy risk scoring framework integrating structural similarity, entity-level sensitivity analysis, and graph-sensitive weighting. It combines graph neural networks (GNNs), natural language processing (NLP), and named entity recognition (NER) to enable fine-grained, interpretable, and personalized risk modeling, supporting cross-dimensional joint assessment and visualizable insights. Contribution/Results: Evaluated on real-world datasets, the framework achieves an overall average risk score of 0.478 and 0.501 in graph-centric scenarios. A user study confirms high usability: 85% of participants rated its clarity and practicality as satisfactory. This work establishes a novel, dynamic, and scalable paradigm for social privacy risk assessment.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Privacy
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

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 include email, date of birth, and mobile number. Our user study with 100 participants shows 85% rated the dashboard as clear and actionable, confirming CPRS's practical utility. This work enables personalized privacy risk insights and contributes a holistic, scalable methodology for privacy management. Future directions include incorporating temporal dynamics and multimodal content for broader applicability.

Problem

• Quantify privacy risks in social networks using user attributes and graphs
• Assess privacy threats from shared content and social connections
• Develop a unified risk scoring framework for privacy management

Innovation

• Integrates user attributes, social graphs, text analysis
• Computes unified privacy risk score via multi-dimensional aggregation
• Validated on real datasets with actionable user dashboard