A Context-Aware and Explainable Model for Privacy Protection in Social Networks
DOI:
https://doi.org/10.61704/pr.719Keywords:
Social Network Privacy, Explainable Artificial Intelligence, Context-Aware Privacy, Image Privacy Protection, Preserving AIAbstract
With the rapid growth of social networking platforms, users share their content online that can potentially compromise their privacy. Images shared on social networks can expose information such as their faces, identity documents, locations, health conditions, screens, license plates, children, private environments, and more. While various approaches to the field of social network image privacy-preserving, such as access control, encryption, face blurring, adversarial perturbation, and deep learning-based privacy prediction have been proposed, each approach suffers from three major limitations: insufficient context awareness, weak explainability, and poor balance between privacy protection and content utility. To address these limitations, in this paper, we propose CA-XPrivNet: a context-aware and explainable framework for the privacy protection of content on social networks. CA-XPrivNet includes spatial-domain and frequency-domain methods to sanitize privacy of the shared images while preserving the content that is not sensitive to the social network user. Furthermore, a multimodal risk estimation module determines the risk of the image to reveal sensitive information, and the explainability of the framework is achieved through attention maps, Grad-CAM methods, SHAP methods, and privacy-preserving explanation filtering. To evaluate the proposed framework, experiments were conducted using three publicly available social-network image privacy datasets: PrivacyAlert, DIPA, and DIPA2. These datasets provide a variety of privacy-sensitive images that can be used to evaluate the performance of the framework in terms of its ability to classify privacy risks, reduce privacy leakage, preserve image quality, provide high-quality explanations, and provide fast processing times while providing a balance between privacy and content usability. The results of the experiments indicate that the framework can provide adaptive and explainable privacy protection while preserving the usability of non-sensitive content.
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Copyright (c) 2026 Raghad S. Mohamed
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Copyright © 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). You may not alter or transform this work in any way without permission from the authors. Non-commercial use, distribution, and copying are permitted, provided that appropriate credit is given to the authors and Al-Hadba University.
