scholarly journals Sybil-proof Answer Querying Mechanism

2020 ◽  
Author(s):  
Yao Zhang ◽  
Xiuzhen Zhang ◽  
Dengji Zhao
Keyword(s):  
Social Network ◽  
Question Answering ◽  
Cost Minimization ◽  
Impossibility Results ◽  
Reward Mechanisms ◽  
Sybil Attacks

We study a question answering problem on a social network, where a requester is seeking an answer from the agents on the network. The goal is to design reward mechanisms to incentivize the agents to propagate the requester's query to their neighbours if they don't have the answer. Existing mechanisms are vulnerable to Sybil-attacks, i.e., an agent may get more reward by creating fake identities. Hence, we combat this problem by first proving some impossibility results to resolve Sybil-attacks and then characterizing a class of mechanisms which satisfy Sybil-proofness (prevents Sybil-attacks) as well as other desirable properties. Except for Sybil-proofness, we also consider cost minimization for the requester and agents' collusions.

scholarly journals SybilLimit: A Near-Optimal Social Network Defense Against Sybil Attacks

2010 ◽  
Vol 18 (3) ◽  
pp. 885-898 ◽  
Author(s):  
Haifeng Yu ◽  
Phillip B. Gibbons ◽  
Michael Kaminsky ◽  
Feng Xiao
Keyword(s):  
Social Network ◽  
Network Defense ◽  
Sybil Attacks

scholarly journals Context-aware Frame-Semantic Role Labeling

2015 ◽  
Vol 3 ◽  
pp. 449-460 ◽  
Author(s):  
Michael Roth ◽  
Mirella Lapata
Keyword(s):  
Social Network ◽  
Question Answering ◽  
State Of The Art ◽  
Semantic Role ◽  
Context Aware ◽  
Semantic Role Labeling ◽  
Current State ◽  
Sentence Level ◽  
Small Set ◽  
Labeling System

Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling.

scholarly journals Automatic extraction of social network users' attitudes on reproductive behavior issues

2021 ◽  
Vol 12 (4) ◽  
pp. 33-63
Author(s):  
Ирина Евгеньевна Калабихина ◽  
Наталья Валентиновна Лукашевич ◽  
Евгений Петрович Банин ◽  
Камила Винеровна Алибаева ◽  
Софья Михайловна Ребрей
Keyword(s):  
Social Network ◽  
Natural Language ◽  
Reproductive Behavior ◽  
Question Answering ◽  
Automatic Extraction ◽  
Behavior Issues

В данной работе мы представляем специализированный датасет, с разметкой мнений пользователей о репродуктивном поведении. Мы анализируем особенности распределение оценок «за» и «против» по конкретным аспектам репродуктивного поведения. Созданный датасет используется для решения двух задач классификации: классификации сообщений по релевантности изучаемых тем и позиции автора по той или иной теме. Для классификации сообщений используются классические методы машинного обучения, а также нейросетевая модель BERT. Лучшие результаты классификации в обеих задачах достигаются на основе вариантов модели BERT с использованием в классификации пар предложений — варианты NLI (natural language inference — вывод по тексту) и QA (question-answering — вопросно/̄ответный подход). Кроме того, созданный датасет позволяет сделать содержательные выводы по вопросам отношения пользователей сети ВКонтакте к вопросам репродуктивного поведения. Выявлено, что феномен сознательной бездетности активно представлен в сети, а многодетность остается слабо распространенной моделью поведения. В рамках пронаталистской политики важно формировать позитивное общественное мнение о родительстве, смягчать дефицит времени у родителей.

scholarly journals Salmon: Robust Proxy Distribution for Censorship Circumvention

2016 ◽  
Vol 2016 (4) ◽  
pp. 4-20
Author(s):  
Frederick Douglas ◽  
Weiyang Pan ◽  
Matthew Caesar ◽  
Keyword(s):  
Social Network ◽  
Streaming Video ◽  
Internet Usage ◽  
The Internet ◽  
Covert Channels ◽  
Proxy Servers ◽  
The Public ◽  
Low Performance ◽  
Censorship Circumvention ◽  
Sybil Attacks

Abstract Many governments block their citizens’ access to much of the Internet. Simple workarounds are unreliable; censors quickly discover and patch them. Previously proposed robust approaches either have non-trivial obstacles to deployment, or rely on low-performance covert channels that cannot support typical Internet usage such as streaming video. We present Salmon, an incrementally deployable system designed to resist a censor with the resources of the “Great Firewall” of China. Salmon relies on a network of volunteers in uncensored countries to run proxy servers. Although any member of the public can become a user, Salmon protects the bulk of its servers from being discovered and blocked by the censor via an algorithm for quickly identifying malicious users. The algorithm entails identifying some users as especially trustworthy or suspicious, based on their actions. We impede Sybil attacks by requiring either an unobtrusive check of a social network account, or a referral from a trustworthy user.

scholarly journals Multi-Winner Contests for Strategic Diffusion in Social Networks

2019 ◽  
Vol 33 ◽  
pp. 6154-6162 ◽  
Author(s):  
Wen Shen ◽  
Yang Feng ◽  
Cristina V. Lopes
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Real World ◽  
Numerical Experiments ◽  
Budget Constraint ◽  
Individual Rationality ◽  
Proper Design ◽  
Reward Mechanisms ◽  
The Way

Strategic diffusion encourages participants to take active roles in promoting stakeholders’ agendas by rewarding successful referrals. As social media continues to transform the way people communicate, strategic diffusion has become a powerful tool for stakeholders to influence people’s decisions or behaviors for desired objectives. Existing reward mechanisms for strategic diffusion are usually either vulnerable to falsename attacks or not individually rational for participants that have made successful referrals. Here, we introduce a novel multi-winner contests (MWC) mechanism for strategic diffusion in social networks. The MWC mechanism satisfies several desirable properties, including false-name-proofness, individual rationality, budget constraint, monotonicity, and subgraph constraint. Numerical experiments on four real-world social network datasets demonstrate that stakeholders can significantly boost participants’ aggregated efforts with proper design of competitions. Our work sheds light on how to design manipulation-resistant mechanisms with appropriate contests.

Sign in / Sign up

Export Citation Format

Share Document