Sybil-Resilient Online Content Voting

By Dinh Nguyen Tran, Bonan Min, Jinyang Li, L. Subramanian. 2009

340 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Mitigating Sybils in Federated Learning Poisoning (2018, background): “These approaches use auxiliary interaction information such as an underlying social network [33], or involve detecting and rejecting malicious behavior [34].”
Proactively Accountable Anonymous Messaging in Verdict (2012, background): “Dynamic group management schemes could leverage existing Sybil prevention techniques [47, 53, 54], but we do not consider such approaches herein.”
An analysis of social network-based Sybil defenses (2010, methodology): “A number of schemes have been proposed that attempt to defend against Sybils in a social network by using properties of the social network’s structure [7,29,32,33].”
Aiding the Detection of Fake Accounts in Large Scale Social Online Services (2012, unknown): “In the rest, we discuss our findings and prior OSN Sybil defense proposals.”
Combating Attacks and Abuse in Large Online Communities (2016, background): “In the context of online social networks, most Sybil detection systems rely on social graphs [247, 246, 217, 221, 69, 58].”
Hang with your buddies to resist intersection attacks (2013, unknown): “To support open-ended groups, Buddies could build on one of the many Sybil attack resistance schemes, such as those based on social networks [48,54]—or could simply rate-limit Sybil attacks via some “barrier to entry,” e.”
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage (2016, background): “SumUp [26] and Iolaus [19] adapt this to content rating settings.”
Strength in Numbers: Robust Tamper Detection in Crowd Computations (2015, background): “Prominent among them are DSybil [42], SumUp [34], and Iolaus [23], which work by preventing or discounting votes based on trusted guides (DSybil) or the social network (SumUp and Iolaus).”
The socialbot network: when bots socialize for fame and money (2011, background): “To defend against the Sybil attack, recent research propose to exploit the implicit social network among the nodes of a DHT in order to detect, and thereof limit, the number of Sybil nodes it may contain [15, 66, 67, 77, 78].”
Preserving Link Privacy in Social Network Based Systems (2012, background): “Second we define general metrics for characterizing the utility and privacy of perturbed graphs.”
SoK: The Evolution of Sybil Defense via Social Networks (2013, background): “Several protocols that embrace this style of sybil de− fense have since been proposed [7], [10], [35], [42], [45], [46] and higher−level distributed applications that rely on them are beginning to emerge [18], [19], [26], [36].”
Uncovering social network sybils in the wild (2011, methodology): “SybilGuard [24], SybilLimit [23], SybilInfer [3], and SumUp [17] are all algorithms for performing decentralized detection of Sybil nodes on social graphs.”
Defending against Sybil Devices in Crowdsourced Mapping Services (2015, background): “The mo st popular approach is graph-based where the key assumption is that Sybils have difficulty to connect to real users [10, 14, 46, 52 , 53].”
Adversarial Web Search (2011, unknown): “[223] describe a voting method that analyzes the social network of users, and gives less weight to votes from users that are not well connected to other users.”
DAWN: Dynamic Adversarial Watermarking of Neural Networks (2019, background): “must require providing information that maximizes difficulty of creating trusted accounts, e.g., verified phone number or credit card, to mitigate this threat. Also, Sybils-detection techniques exist [43, 46] and it is possible to link Sybils accounts by examining querying patterns and IP addresses ”
SybilLimit: A Near-Optimal Social Network Defense Against Sybil Attacks (2010, methodology): “Also assuming global knowledge and further focusing on scenarios where only honest identities are seeking to be accepted, SumUp [26] uses adaptive maximum .ow on the social network to bound the number of sybil identities (voters) accepted per attack edge within .”