There has been a flurry of research on leveraging social networks to defend against multiple identity, or Sybil, attacks. A series of recent works does not try to explicitly identify Sybil identities and, instead, bounds the impact that Sybil identities can have. We call these approaches Sybil tolerance; they have shown to be effective in applications including reputation systems, spam protection, online auctions, and content rating systems. All of these approaches use a social network as a credit network, rendering multiple identities ineffective to an attacker without a commensurate increase in social links to honest users (which are assumed to be hard to obtain). Unfortunately, a hurdle to practical adoption is that Sybil tolerance relies on computationally expensive network analysis, thereby limiting widespread deployment.

Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends ’ information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.

Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends ’ information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.

Abstract—Today, the data exchanged over online social networks (OSNs) represents a significant fraction of Internet traffic. However, OSN content is different from more traditional web content, as it is more likely to be generated at the edge of the network, to be exchanged within a local geographic region, and to possess a more even popularity distribution with fewer popular objects. Unfortunately, most OSNs still use largely centralized approaches to distribute content (e.g., CDNs and web caches), resulting in lower performance due to the different workload. In this paper, we take a first step towards addressing this situation by proposing WebCloud, a content distribution system for OSNs that works by repurposing client web browsers to help serve content to others. When a user browses content, WebCloud tries to serve the request from one of that user’s friends ’ browsers, instead of from the OSN directly. Unlike other systems, WebCloud works with existing browsers and does not require any plug-ins, and therefore can be directly applied to today’s OSNs. We demonstrate the practicality of WebCloud with microbenchmarks, simulations of a Facebook deployment, a real-world deployment, and evaluations of a proof-of-concept iOS app. I.

Thwarting large-scale crawls of user profiles in online social networks (OSNs) like Facebook and Renren is in the interest of both the users and the operators of these sites. OSN users wish to maintain control over their personal information, and OSN operators wish to protect their business assets and reputation. Existing rate-limiting techniques are ineffective against crawlers with many accounts, be they fake accounts (also known as Sybils) or compromised accounts of real users obtained on the black market. We propose Genie, a system that can be deployed by OSN operators to defend against crawlers in large-scale OSNs. Genie exploits the fact that the browsing patterns of honest users and crawlers are very different: even a crawler with access to many accounts needs to make many more profile views per account than an honest user, and view profiles of users that are more distant in the social network. Experiments using real-world data gathered from a popular OSN show that Genie frustrates large-scale crawling while rarely impacting honest users; the few honest users who are affected can recover easily by adding a few friend links.