Privacy-Preserving Photo Sharing on Online Social Networks: A Review

Abstract

Online social networking applications and their services have become an essential part of the human lifestyle. These social services help online users in different ways, such as through social visibility, content sharing, communication, promotions, etc. In the same way, the services of online social networks (OSNs) pose privacy risks for their users. The service of photo sharing in OSNs, in particular, causes leakage of online social users' personal information. It's critical to comprehend previous studies that looked at privacypreserving photo sharing and whether users have their privacy protected when sharing photos. The goal of this review is to bridge the gap between the growing demand for image sharing via OSNs and individualized privacy requirements. This effort presents a comprehensive analysis of "privacy-preserving" technologies that specifically address contemporary privacy concerns associated with sharing images on online social networks (OSNs). This study presents a comprehensive analysis framework that focuses on the complete lifecycle of image sharing on online social networks (OSNs). This work presented a review framework called Privacy-Preserving Photo Sharing (PPPS) by categorizing previous works into three stages related to online secure photo sharing. Preprocessing, privacy settings, and photo publishing are the stages used in this survey's design to secure photo sharing. This framework aims to tackle the many privacy challenges and propose appropriate solutions in this multidisciplinary domain. During each phase, we analyse common user behaviours connected to sharing, the privacy concerns that arise from those behaviours, and evaluate representative solutions that are proposed by previous works.

Generation of Face Privacy-Protected Images Based on the Diffusion Model

“…During the second stage, the model utilizes partial conditions and modifies the loss function to incorporate the portion generated unconditionally. Equation (11) displays the loss function.…”

“…However, these methods directly remove facial information, resulting in compromised visual quality and reduced reusability. Subsequently, researchers shifted towards approaches centered on facial replacement [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. This strategy aims to obfuscate the identity of the original face by substituting parts of it while retaining some facial features to maintain usability.…”