Abstract-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 investigate whether Facebook users have become more private in recent years. Specifically, we examine if there have been any important trends in the information Facebook users reveal about themselves on their public profile pages since early 2010. To this end, we have crawled the public profile pages of 1.4 million New York City (NYC) Facebook users in March 2010 and again in June 2011.We have found that NYC users in our sample have become dramatically more private during this period. For example, in March 2010 only 17.2% of users in our sample hid their friend lists, whereas in June 2011, just 15 months later, 52.6% of the users hid their friend lists. We explore privacy trends for several personal attributes including friend list, networks, relationship, high school name and graduation year, gender, and hometown. We find that privacy trends have become more pronounced for certain demographics. Finally, we attempt to determine the primary causes behind the dramatic decrease in the amount of information Facebook users reveal about themselves to the general public.
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.
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