Lawmakers, children's advocacy groups and modern society at large recognize the importance of protecting the Internet privacy of minors (under 18 years of age). Online Social Networks, in particular, take precautions to prevent third parties from using their services to discover and profile minors. These precautions include displaying only minimal information in registered minors' public profiles, not listing minors when searching for users by high school or city, and banning young children from joining altogether. In this paper we show how an attacker can circumvent these precautions. We develop efficient crawling and data mining methodologies to discover and profile most of the high school students in a targeted high school. In particular, using Facebook and for a given target high school, the methodology finds most of the students in the school, and for each discovered student infers a profile that includes significantly more information than is available in a registered minor's public profile. Such profiles can be used for many nefarious purposes, including selling the profiles to data brokers, large-scale automated spear-phishing attacks on minors, as well as physical safety attacks such as stalking, kidnapping and arranging meetings for sexual abuse. Ironically, the Children's Online Privacy Protection Act (COPPA), a law designed to protect the privacy of children, indirectly facilitates the approach. In order to bypass restrictions put in place due to the COPPA law, some children lie about their ages when registering, which not only increases the exposure for themselves but also for their non-lying friends. Our analysis strongly suggests there would be significantly less privacy leakage if Facebook did not have age restrictions.

User Generated Content (UGC) video applications, such as YouTube, are enormously popular. UGC systems can potentially reduce their distribution costs by allowing peers to store and redistribute the videos that they have seen in the past. We study peer-assisted UGC from three perspectives. First, we undertake a measurement study of the peer-assisted distribution system of Tudou (a popular UGC network in China), revealing several fundamental characteristics that models need to take into account. Second, we develop analytical models for peer-assisted distribution of UGC. Our models capture essential aspects of peer-assisted UGC systems, including system size, peer bandwidth heterogeneity, limited peer storage, and video characteristics. We apply these models to numerically study YouTube-like UGC services. And third, we develop analytical models to understand the rate at which users would install P2P client applications to make peer-assisted UGC a success. Our results provide a comprehensive study of peer-assisted UGC distribution, exposing its fundamental characteristics and limitations.

YouTube uploaders are the central agents in the YouTube phenomenon. We conduct extensive measurement and analysis of YouTube uploaders. We estimate YouTube scale and examine the uploading behavior of YouTube users. We demonstrate the positive reinforcement between on-line social behavior and uploading behavior. Furthermore, we examine whether YouTube users are truly broadcasting themselves, via characterizing and classifying videos as either user generated or user copied.