# Evaluating Secure Methodology for Photo Sharing in Online Social Networks
## 1 Introduction  
Over the years, people used to search for beautiful memories in their photo albums, and with the advancement of technology and the emergence of smart devices, a person opens his phone and sees the videos he recorded and the photos he took. Some consider that capturing and recording beautiful moments and sharing them on Online Social Networking (OSN) with family and friends simulates experiencing them with them [1]; because it is the archive that we turn to whenever we want to know the news and events of important people, especially when it is with loved ones and friends who are far from the eye or busy in their lives. The OSN is defined as means of communication through which the user creates an account that enables him to communicate via the Internet with other people electronically [2]. To share information, ideas, opinions, messages, and other written, visual, audio, and file content. Examples of these platforms are Facebook, Twitter, Snapchat, and Instagram. For many people, posting a tweet on Twitter, creating a Facebook page, choosing the best hashtags to attach to their Instagram photos, or posting a cute video on YouTube has become a very easy and normal activity. If a person is an expert, then they may do the above four activities at the same time. However, with the constantly flowing development in the OSN and the emergence of many new sites and applications, we can notice the development of the lifestyle of users based on what is happening within these sites. The spread of the OSN imposed by technological development has led to the explosion of a new conflict that is raging vigorously, both sides of which are clinging to their personal freedoms, and violators of privacy who do not hesitate to exploit every opportunity to benefit from them and expose the private lives of others [3, 4, 5, 6, 7]. Sharing photos by unauthorized individuals is considered a privacy breach [8]. Every person has the right to permit posting a picture of himself or his children, whether it is asingle or shared picture, on social media. This controls the nature of the posted photos and helps increase privacy protection. Developing a feature added to OSN that would first seek permission before posting any photo may reduce such cases of cyberbullying and breach of privacy. Accordingly, this paper evaluates empirically our proposed methodology in [9]. In particular, this paper introduces an implementation of our proposed methodology in [9] as an application that works on smart device systems for both platforms IOS and Android. It is designed to allow people to know the photos posted for them and decide during the publishing process. By recognizing the people in the photos and determining their identity through facial recognition, the facial recognition technique can identify and verify a person in pictures in real-time. After completing the identification process, the application returns to the database to send a prior notice to the people to take permission to publish the photos or not. The application is written in Flutter language. This language is a toolkit that helps create mobile, web, and desktop applications from a single code base [10]. For facial recognition, we employ the deep learning technique, which is a branch of machine learning, with machine learning itself being a branch of artificial intelligence [11]. To be more precise, the convolutional neural network (CNN), which is one of the main categories of computer vision, will be used to perform image recognition and image classifications [12]. This CNN is used in the application to detect the faces in the image and then identify people's identity. Our principal contribution is to prevent the publication of any photo without the permission of the owner of the photo. To the best of our knowledge, this has not been discussed before. Then, we carried out a real study to demonstrate the effectiveness of the proposed system. The results demonstrate that our proposal ensures a high level of privacy protection.