{"ID":2892693,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.15124","arxiv_id":"2507.15124","title":"Comprehensive Privacy Risk Assessment in Social Networks Using User Attributes Social Graphs and Text Analysis","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.","short_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,...","url_abs":"https://arxiv.org/abs/2507.15124","url_pdf":"https://arxiv.org/pdf/2507.15124v1","authors":"[\"Md Jahangir Alam\",\"Ismail Hossain\",\"Sai Puppala\",\"Sajedul Talukder\"]","published":"2025-07-20T21:18:50Z","proceeding":"cs.SI","tasks":"[\"cs.SI\"]","methods":"[]","has_code":false}