Research On Location Privacy Protection Technology In Social Network Image Sharing

The development of mobile Internet technology and the improvement of hardware level have brought great development to the online social network that has already occupied the top seats of the network service share,while serious privacy leakage problem also followed.On the one hand,due to the imperfect method of privacy protection(such as the clumsy procedure in privacy policy settings,and the lack of means for protection in multimedia information like image and video),and the public's lack of awareness of privacy protection,traditional visual privacy leakage problem still exists;On the other hand,there are hidden valuable information in the privacy data of users except the information that can be directly accessed.The development of big data and artificial intelligence technology enable enterprises and organizations to mine deeper information from private data.A wide range of potential information that varies from user behavior to market analysis can guide business activities and may also be used for illegal activities.Under the combination of the old and new privacy threats,there is still a long way to go to solve the privacy protection problem.Location privacy leakage in image content remains a blind spot among numerous privacy protection studies.The existing researches on location privacy protection is mainly aimed at the real geographical location of the user;however,the image is a format with rich information itself whose contents may indicate the location of the photo shot;In addition,with the popularity of mobile devices,people can post photos in online social network whenever or wherever they wants,which brings higher real-time character to the location privacy in image and further increases the threat of location privacy disclosure.There are two key points in this location privacy problem: the first one is how to find out privacy-sensitive objects and their categories from many images related to the scene;then,after solving the previous problem,how to determine the standard of privacy correlation degree,in other words,to what extent can an object indicates its location.To solve this problem,this thesis proposes a scheme that can protect the location privacy in social network image sharing,and realizes image location privacy region protection from two aspects: firstly,in the scheme that against human vision,this thesis uses deep learning based image segmentation,deep feature extraction and feature clustering technology to determine the potential object regions and their categories in the image data set;then,this scheme proposes two privacy correlation judgment policies according to the characteristics of location image data set,which enables the scheme to assign corresponding privacy level to different categories;online learning privacy classifier is adopted for the sake of making privacy standards more flexible and reasonable,and the privacy judgment standard can be dynamically changed based on user feedback;finally,this thesis use exemplar-based image inpainting method to remove privacy region in image for the purpose of protect privacy from human vision.In the scheme that against machine vision,this thesis proposes an image perturbation generating algorithm based on adversarial examples,which can make the images unable to be obtained useful information by the deep learning-based object detection model after being added specific perturbations,and people can hardly be aware of these perturbations.At last,the experiments are conducted on both privacy protection schemes and the results are analyzed,which demonstrate the proposed privacy protection schemes can protect the location privacy information in images.