# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 2 Related Works  
Facial privacy protection has attracted significant attention in recent years due to the proliferation of facial data on online platforms and the increasing risks of misuse. Various  
Figure 1: Overview of the interaction between image owners and consumers in the proposed system. techniques have been proposed to address these challenges, each targeting different aspects of facial data protection. Some approaches focus on image modification to protect facial privacy, while others emphasize user consent and access control mechanisms. In this section, we review several existing works on facial privacy protection, highlighting their key features and limitations. Table 1 presents a comparative overview of approaches focused on image modification, while Table 2 provides a comparison of approaches that prioritize user consent and access control. In the context of image modification to protect facial privacy, [PERSON] et al. (2017) proposed the Diffusion-Based Facial Privacy Protection Network (DIFP), which employs diffusion models to generate photorealistic and privacy-preserving images. DIFP ensures the reversibility of protected images, enabling the restoration of the original faces when necessary. Similarly, [PERSON] et al. (2018) introduced Diff-Privacy, which produces visually realistic and diverse anonymized images while maintaining the ability to recover original identities as needed. Although both approaches ensure reversibility, the restored images are not identical to the original images at the pixel level. [PERSON] et al. (2019) proposed PluGeN4 Faces, a method for decoupling facial attributes from identity in StyleGAN's latent space, allowing for precise attribute manipulation while maintaining image realism. Although not explicitly designed for privacy protection, this approach could be adapted to anonymize facial images by altering identifiable attributes, such as age, hairstyle, or beard. [PERSON] et al. (2019) introduced the Digital Mask (DM), which uses 3D reconstruction and deep-learning algorithms to irreversibly anonymize facial features while preserving disease-relevant attributes. Unlike DIFP and Diff-Privacy, both PluGeN4 Faces and DM lack the capability to reverse to the original image. Nevertheless, these methods offer promising alternatives for generating privacy-preserving facial images. Another key aspect of facial privacy protection is access control, which involves obtaining user consent and granting users control over the usage of their facial data. [PERSON] et al. (2019) introduced a system where social media users depicted in a photo are notified and allowed to participate in decisions about photo sharing to prevent privacy breaches. Similarly, [PERSON] et al. (2019) proposed an automatic tagging framework that restricts photo access based on the identities of tagged individuals. Although these approaches focus on \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Key Features** & **Face Reversibility** & **Multi User Access Control** \\ \hline DIFP ([PERSON] et al., 2017) & Diffusion-based facial privacy protection network & Reversible but not pixel-perfect & No \\ Diff-Privacy ([PERSON] et al., 2017) & Privacy-preserving facial image generation & Reversible but not pixel-perfect & No \\ PluGeN4 Faces ([PERSON] et al., 2017) & Person’s attribute manipulation & Irreversible & No \\ DM ([PERSON] et al., 2019) & Irreversible anonymization of facial features & Irreversible & No \\ SnapSafe ([PERSON] et al., 2019) & Selective facial encryption for single-party scenarios & Pixel-perfect and reversible & No \\ Proposed System & Dynamic multi-user access control for facial privacy protection & Pixel-perfect and reversible & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative overview of proposed approaches with some existing works in facial privacy protection.