We have conducted an extensive experimental study and the results demonstrate efficiency and effectiveness in our approach. While photo sharing sites like Flickr provide efficient tools for setting up an online album, users who want to maintain a certain level of privacy generally only get rudimentary access control. Since descriptive tags are widely used in photos and the semantic web provides a common means of sharing social media information as linked data, we believe that a better access control mechanism can be provided by combining the two. Based on this idea, we set up and describe in this document a system that allows users to create an expressive access control policy for their photos on the web using linked tags and data.
Unfortunately, the faces of stakeholders, depicted in shared photos, can be exposed to unexpected viewers. This document proposes an approach to prevent such leaks based on access control and facial recognition. Every time a photo is sent, all interested parties are recognized and their faces are hidden from viewers. At the same time, stakeholders are informed of the photo and may decide to reveal their own faces to some suitable viewers later.
Therefore, this document proposes an auction-based privacy mechanism to manage user privacy when information related to multiple people is at stake. We propose to have a software agent who acts on behalf of each user to open privacy auctions, learn about people’s subjective privacy reviews over time, and make an offer to respect their privacy. With faster and more reliable internet connections, online video sharing sites have been a success, especially with greater active user engagement. That’s why we’ve compiled a list of some of the best video sharing sites and applications with content from original artists and creators, as well as users.
Many of the photo sharing applications on these sites allow users to annotate photos with those on them. Several researchers have explored the social applications and privacy issues of online photo sharing sites, but few have investigated the privacy issues of photo sharing on social media. In this document, we begin by examining some of our findings from a number of focus groups on social media photo privacy. Traditionally, privacy violations are perceived as the malfunction of a particular system. On online social media, however, privacy violations are not necessarily a malfunction of a system, but a by-product of its operation.
Popular photo sharing sites have attracted millions of people and have helped build massive social media in cyberspace. Unlike traditional social relationships, users actively communicate in groups where common interests are shared about certain types of events or themes captured in photos and videos. Contributing images to an interest group would greatly promote user interactions and expand their social media. In this work, we plan to make automatic user image recommendations to suitable shared photo groups. To this end, we start by analyzing user annotations and modeling shared images in a group. Both the visual content and the annotation context are integrated to understand the events or themes in those images.