Enhancing Privacy in Social Media Photo Sharing: A User-Centric Framework Integrating Multi-Party Consent and Blockchain Technologies
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Abstract
The widespread sharing of photographs on social networks has redefined digital communication, thereby raising privacy concerns related to control over visual data. Unlike textual content, photos involve multiple stakeholders, which complicates ownership and consent. This research addresses these challenges by proposing a Privacy Control Model that incorporates multi-party consent mechanisms, blockchain-based permission tracking, and user-centric design to enhance privacy management in photo sharing. Using the Facebook Social Network Dataset (SNAP), the model was evaluated across three key metrics: unauthorized viewing probability tagging) conflict rate) and user satisfaction ( . The results demonstrated a 70-79% reduction in unauthorized views, with decreasing from 38% (DPSM) to 8%. Tagging conflicts were reduced by 52-75%, with dropping from 48% (DPSM) to 12%. User satisfaction increased significantly from 2.9/5 (DPSM) to 4.7/5, reflecting the effectiveness of the model’s intuitive tools and transparency features. While the model proves robust, limitations include potential scalability issues owing to the computational requirements of facial recognition and blockchain, as well as dependence on user compliance and algorithm accuracy. Future work will address these concerns and explore broader applications for other media types, such as videos and live streams. This study underscores the potential of technology-driven, user-centric solutions to balance privacy and connectivity in photo sharing, paving the way for enhanced digital trust and user autonomy on social networks.
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