Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms

• Published in: Future internet Vol. 17; no. 3; p. 124
• Main Authors: Santoso, Andri, Huda, Samsul, Kodera, Yuta, Nogami, Yasuyuki
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2025
• Subjects: Access control; Biometrics; Consumers; Data encryption; Deep learning; deep-learning applications; Encryption; facial privacy protection; multi-user access control; NGOs; Nongovernmental organizations; online photo platform; Platforms; Privacy; Public domain; selective facial encryption; Social networks; Splitting; Surveillance; Usability
• ISSN: 1999-5903; 1999-5903
• DOI: 10.3390/fi17030124

In the digital age, sharing moments through photos has become a daily habit. However, every face captured in these photos is vulnerable to unauthorized identification and potential misuse through AI-powered synthetic content generation. Previously, we introduced SnapSafe, a secure system for enabling selective image privacy focusing on facial regions for single-party scenarios. Recognizing that group photos with multiple subjects are a more common scenario, we extend SnapSafe to support multi-user facial privacy protection with dynamic access control designed for online photo platforms. Our approach introduces key splitting for access control, an owner-centric permission system for granting and revoking access to facial regions, and a request-based mechanism allowing subjects to initiate access permissions. These features ensure that facial regions remain protected while maintaining the visibility of non-facial content for general viewing. To ensure reproducibility and isolation, we implemented our solution using Docker containers. Our experimental assessment covered diverse scenarios, categorized as “Single”, “Small”, “Medium”, and “Large”, based on the number of faces in the photos. The results demonstrate the system’s effectiveness across all test scenarios, consistently performing face encryption operations in under 350 ms and achieving average face decryption times below 286 ms across various group sizes. The key-splitting operations maintained a 100% success rate across all group configurations, while revocation operations were executed efficiently with server processing times remaining under 16 ms. These results validate the system’s capability in managing facial privacy while maintaining practical usability in online photo sharing contexts.