Obtaining user opinion (using votes) is essential to ranking user-generated online content. However, any content voting system is susceptible to the Sybil attack where adversaries can out-vote real users by creating many Sybil identities. In this paper, we present SumUp, a Sybilresilient vote aggregation system that leverages the trust network among users to defend against Sybil attacks. SumUp uses the technique of adaptive vote flow aggregation to limit the number of bogus votes cast by adversaries to no more than the number of attack edges in the trust network (with high probability). Using user feedback on votes, SumUp further restricts the voting power of adversaries who continuously misbehave to below the number of their attack edges. Using detailed evaluation of several existing social networks (YouTube, Flickr), we show SumUp’s ability to handle Sybil attacks. By applying SumUp on the voting trace of Digg, a popular news voting site, we have found strong evidence of attack on many articles marked “popular ” by Digg. 1

Users of mobile devices increasingly expect Internet connectivity wherever they travel. Despite the roll-out of wide-area wireless broadband, many devices, such as iPods and portable game stations still rely on local area WiFi networks to obtain connectivity. Even smart phones may prefer WiFi over 3G and WiMAX to improve the performance of high throughput applications or to avoid data charges. Fortunately, there is often a large selection of WiFi service providers to choose from. For example JiWire, 1 a hotspot directory, reports 400 to 1000 commercial WiFi networks in each of the top ten U.S. metropolitan areas. Nevertheless, in a study of commercial hotspots in Seattle, we found significant diversity in performance and functionality due to differing back-haul capacity, port blocking, and poorly functioning WiFi access

The success of the Internet has significantly changed how people interact with each other. Rich social interactions nowadays take place online. Users read, shop, chat, work, and play on the Internet. However, unlike

We open the new academic year with Haifeng Yu’s article on overcoming sybil attacks using social networks. In a sybil attack, a malicious user assumes multiple identities, and uses them to pose as multiple users. Sybil attacks are a threat of the new millennium – they arise in Internet-based distributed systems with a dynamic user population. Indeed, such attacks were not a concern in traditional distributed systems, where the set of participating processes was statically pre-defined. Sybil attacks are inherently difficult to deal with in systems where users do not wish to disclose binding private information, like credit card numbers. A recent popular approach for overcoming sybil attacks is using social networks. Intuitively, even if a malicious user can create many identities, he will have a hard time getting many honest users to befriend all of them in a social network. Thus, the graph structure of a social network can assist in revealing sybil nodes. In this column, Haifeng Yu presents a tutorial on how social networks can be leveraged to defend against sybil attacks, and a survey of recent suggestions employing this approach. Though Haifeng tackles the problem from a theoretical standpoint, (proving formal bounds etc.), this direction has garnered more attention from the systems community, perhaps because sybil attacks are perceived as a real threat for which social networks can provide a viable solution. Yet it appears that much theory for sybil defense using social networks

Consider a set of players that are interested in collectively evaluating a set of objects. We develop a collaborative scoring protocol in which each player evaluates a subset of the objects, after which we can accurately predict each players’ individual opinion of the remaining objects. The accuracy of the predictions is near optimal, depending on the number of objects evaluated by each player and the correlation among the players ’ preferences. A key novelty is the ability to tolerate malicious players. Surprisingly, the malicious players cause no (asymptotic) loss of accuracy in the predictions. In fact, our algorithm improves in both performance and accuracy over prior state-of-the-art collaborative scoring protocols that provided no robustness to malicious disruption.

Wi-Fi clients can obtain much better performance at some commercial hotspots than at others. Unfortunately, there is currently no way for users to determine which hotspot access points (APs) will be sufficient to run their applications before purchasing access. To address this problem, this paper presents Wifi-Reports, a collaborative service that provides Wi-Fi clients with historical information about AP performance and application support. The key research challenge in Wifi-Reports is to obtain accurate user-submitted reports. This is challenging because two conflicting goals must be addressed in a practical system: preserving the privacy of users ’ reports and limiting fraudulent reports. We introduce a practical cryptographic protocol that achieves both goals, and we address the important engineering challenges in building Wifi-Reports. Using a measurement study of commercial APs in Seattle, we show that Wifi-Reports would improve performance over previous AP selection approaches in 30%-60 % of locations. Categories and Subject Descriptors:

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Most existing large-scale networked systems on the Internet such as peer-to-peer systems are vulnerable to Sybil attacks where a single adversary can join the system using many fake identities. One promising defense is to perform social-network based node admission control to bound the number of Sybil identities admitted; the assumption here is that an adversary has very few social links with honest users (attack edges). SybilLimit and SumUp, two recent works, can restrict the number of Sybil identities admitted per attack edge to O(log n) with high probability; while SybilLimit is a decentralized protocol, SumUp is a centralized approach where the graph is known. In this paper, we propose an optimal decentralized admission control protocol that admits O(1) Sybil identities (with high probability) in an expander-like social network in the face of O(1) attack edges. In the face of o ( n log n) attack edges, our protocol admits O(log n) Sybil identities per attack edge with high probability. In other words, when the number of attack edges are small, we can design an optimal admission control protocol. This improves over existing results by a factor log n. We also demonstrate the effectiveness of our protocol experimentally on real-world social networks and synthetic topologies. 1

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has played a central role in the design of open distributed systems that span distinct administrative domains. When components of a distributed system can assess the trustworthiness of their peers, they are in a better position to interact with them. There are numerous examples of distributed systems that employ trust inference techniques to regulate the interactions of their components including peer-to-peer file sharing systems, web site and email server reputation services and web search engines. The recent rise in popularity of Online Social Networking (OSN) services has made an additional dimension of trust readily available to system designers: social trust. By social trust, we refer to the trust information embedded in social links as annotated by users of an OSN. This thesis ’ overarching contribution is methods for employing social trust embedded in OSNs to solve two distinct and significant problems in distributed information systems. The first system proposed in this thesis assesses the ability of OSN users to correctly classify online identity assertions. The second system assesses the ability of OSN users to correctly configure devices that classify spamming hosts. In both systems, an OSN user