Since the beginning of the COVID-19 pandemic, instructors and students worldwide have been faced with the challenges of shifting from face-to-face to remote education. Due to universities short notice closure, synchronous online instruction through videoconferencing platforms has been the favoured choice for most instructors. In this paper, we report on our experience teaching an online course using Zoom videoconferencing platform. We conduct a two-fold analysis of online synchronous teaching: 1) We first compare the engagement and feedback features offered by Zoom with traditional classroom interactions. We show that some features are challenging to transpose to traditional face-to-face classroom setting while others may require additional hardware and software. 2) We analyze Zoom's attention tracker tool and evaluate the effectiveness of the attentiveness score as an effective grading metric. Our results show no apparent relationship between the students' attention as measured by Zoom and their performance. We also show that the use of recordings increases while attention decline during live classes.

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.

We perform a large-scale topology mapping and geolocation study for China's Internet. To overcome the limited number of Chinese PlanetLab nodes and looking glass servers, we leverage unique features in China's Internet, including the hierarchical structure of the major ISPs and the abundance of IDC data centers. Using only 15 vantage points, we design a traceroute scheme that finds significantly more interfaces and links than iPlane with significantly fewer traceroute probes. We then consider the problem of geolocating router interfaces and end hosts in China. When examining three well-known Chinese geoIP databases, we observe frequent occurrences of null replies and erroneous entries, suggesting that there is significant room for improvement. We develop a heuristic for clustering the interface topology of a hierarchical ISP, and then apply the heuristic to the major Chinese ISPs. We show that the clustering heuristic can geolocate router interfaces with significantly more detail and consistency than can the existing geoIP databases in isolation. We show that the resulting clusters expose several characteristics of the Chinese Internet, including the major ISPs' provincial structure and the centralized interconnections among the ISPs. Finally, using the clustering heuristic, we propose a methodology for improving commercial geoIP databases and evaluate using IDC data center landmarks.

We perform a large-scale study to quantify just how severe the privacy leakage problem is in Facebook. As a case study, we focus on estimating birth year, which is a fundamental human attribute and, for many people, a private one. Specifically, we attempt to estimate the birth year of over 1 million Facebook users in New York City. We examine the accuracy of estimation procedures for several classes of users: (i) highly private users, who do not make their friend lists public; (ii) users who hide their birth years but make their friend lists public. To estimate Facebook users' ages, we exploit the underlying social network structure to design an iterative algorithm, which derives age estimates based on friends' ages, friends of friends' ages, and so on. We find that for most users, including highly private users who hide their friend lists, it is possible to estimate ages with an error of only a few years. We also make a specific suggestion to Facebook which, if implemented, would greatly reduce privacy leakages in its service.

We perform a large-scale topology mapping and geolocation study for China's Internet. To overcome the limited number of Chinese PlanetLab nodes and looking glass servers, we leverage several unique features in China's Internet, including the hierarchical structure of the major ISPs and the abundance of IDCs. Using only 15 vantage points, we design a traceroute scheme that finds significantly more interfaces and links than iPlane with significantly fewer traceroute probes. We then consider the problem of geolocating router interfaces and end hosts in China. We develop a heuristic for clustering the interface topology of a hierarchical ISP, and then apply the heuristic to the major Chinese ISPs. We show that the clustering heuristic can geolocate router interfaces with significantly more detail and accuracy than can the existing geoIP databases in isolation, and the resulting clusters expose the major ISPs' provincial structure. Finally, using the clustering heuristic, we propose a methodology for improving commercial geoIP databases.