# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 1 Introduction  
Online platforms have transformed communication by facilitating the global sharing of personal content, such as photographs, through social networks and cloud storage. Although these platforms offer significant benefits for convenience and connectivity, they also pose substantial privacy risks [1]. Shared content frequently includes sensitive facial data, exposing individuals to potential privacy threats in the public domain. For instance, facial recognition technologies can exploit this data for biometric-based surveillance [2]. Biometric surveillance involves the collection and analysis of biometric data, such as facial images, to identify and monitor individuals [3]. Since facial images inherently carry the risk of unauthorized identification, their widespread availability on online platformsincreases privacy concerns, especially when combined with other data sources, such as online activities or location metadata [4]. In addition, analyzing facial images alongside contextual elements, such as background details, can inadvertently reveal sensitive information, including an individual's location or daily routines. These vulnerabilities have been exploited in real-world incidents, where perpetrators used facial details from social media to locate and harm individuals [5]. Furthermore, these concerns have become increasingly urgent due to recent advances in deep-learning generative models, particularly their ability to create and manipulate realistic facial data. For example, [PERSON] et al. [6] introduced StyleGAN, a model that can generate highly realistic human faces. Similarly, [PERSON] et al. [7] developed a face-swap technique that can seamlessly replace faces in both images and videos. [PERSON] et al. [8] further proposed a framework for generating videos of people speaking from a single static image, demonstrating the rapid progression in video synthesis. Despite their impressive capabilities, these technological advancements pose significant threats to individual privacy and societal integrity due to potential misuse, such as impersonation and misinformation. Although progress has been made in the detection of synthetic content [9; 10; 11], the rapid advancement of generative models continues to challenge existing detection methods as they generate increasingly realistic images that are often indistinguishable from authentic ones, even by human observers. This ongoing challenge underscores the urgent need for proactive privacy protection strategies that safeguard image data at its source, rather than relying solely on reactive measures to mitigate potential harms. However, despite these growing concerns, many users continue to underestimate the privacy implications of sharing facial images online [12]. This lack of awareness is intensified by the ease of capturing and disseminating images on modern online platforms, resulting in a surge of biometric data available for exploitation [13]. While existing online platforms implement system-level privacy controls to protect biometric data, these measures remain inadequate in addressing facial data privacy challenges. Images shared online are often stored in unencrypted formats and accessible via direct links without authentication, making them vulnerable to unauthorized access and misuse. Furthermore, the persistence of files on online platforms poses another risk, as deleting photos does not guarantee the removal of facial data. Images may still be accessible via their URLs, even after deletion [14]. These limitations highlight the need for file-level facial privacy protection to ensure that facial data remain secure regardless of the image's online accessibility. A common approach to file-level privacy in online platforms involves obscuring faces with emoticons or stickers. While this approach may be sufficient for concealing facial features at the file level, it lacks the sophistication of techniques such as image encryption, which enable the restoration of the original face when necessary.