PRIVACY PRESERVATION IN SOCIAL MEDIA BY IMAGE PROCESSING

Photo sharing is a captivating feature which popularizes Online Gregarious Networks (OSNs). Lamentably, it may leak users’ privacy if they are sanctioned to post, comment, and tag a photo liberatingly. In this paper, we endeavor to address this issue and study the scenario when a utilizer shares a photo containing individuals other than him/her (termed co-photo for short). To obviate possible privacy leakage of a photo, we design a mechanism to enable each individual in a photo be cognizant of the posting activity and participate in the decision making on the photo posting. For this purport, we require an efficient facial apperception (FR) system that can apperceive everyone in the photo. However, more authoritatively mandating privacy setting may limit the number of the photos publicly available to train the FR system. To deal with this dilemma, our mechanism endeavors to utilize users’ private photos to design a personalized FR system concretely trained to differentiate possible photo co-owners without leaking their privacy. We additionally develop a distributed consensus predicated method to reduce the computational intricacy and for fend the private training set. We show that our system is superior to other possible approaches in terms of apperception ratio and efficiency. Our mechanism is implemented as a proof of concept Android application on Facebook’s platform.