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Privacy-Preserving AI Framework for Child Suspicious Activity Recognition With Parental Control and Digital Protection
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Author(s): Vinod Mahor (Maulana Azad National Institute of Technology, India), Jaytrilok Choudhary (Maulana Azad National Institute of Technology, India)and Dhirendra Pratap Singh (Maulana Azad National Institute of Technology, India)
Copyright: 2026
Pages: 28
Source title:
Integrating Parental Consent and Child Engagement With Digital Protection Rules
Source Author(s)/Editor(s): Romil Rawat (LabGeoInf-Research LABoratory in GEOmatics and INFormation systems, National Research Council in Italy, Rome, Italy), Sanjaya Kumar Sarangi (Utkal University, India), A. Samson Arun Raj (Karunya Institute of Technology and Sciences, India), Janet Olivia Richmond (Karunya Institute of Technology and Sciences, India)and Purvee Bhardwaj (Rabindranath Tagore University, India)
DOI: 10.4018/979-8-3373-2716-7.ch009
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Abstract
Ensuring child safety in more and more linked digital contexts provides a double difficulty: properly monitoring questionable behavior while maintaining user privacy and allowing adaptive parental control. Facilitating real-time identification of abnormalities in children's digital activities, the framework uses a hybrid CNN-BiLSTM model to precisely capture both spatial and temporal behavioral patterns. hence addressing important privacy issues., hence delivering real-time notifications and risk evaluations customised to the environment and intensity of the identified actions. Achieving 95% accuracy and good precision, recall, and F1-score, experimental tests using the Kinetics-700 dataset confirm the efficacy of the suggested model. With improved computational efficiency appropriate for real-time applications, the model shows better performance than traditional methods. The inclusion of privacy-preserving technologies does not noticeably affect performance, hence stressing the framework's appropriateness for use in actual digital platforms.
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