Sybil-proof Answer Querying Mechanism
Authors: Yao Zhang, Xiuzhen Zhang, Dengji Zhao
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. |
| Researcher Affiliation | Academia | Yao Zhang , Xiuzhen Zhang and Dengji Zhao Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai Tech University {zhangyao1, zhangxzh1, zhaodj}@shanghaitech.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links, explicit statements, or supplementary materials) for source code. |
| Open Datasets | No | The paper is theoretical and does not describe experiments that utilize training datasets. |
| Dataset Splits | No | The paper is theoretical and does not discuss experimental data splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments requiring specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments or implementations that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training settings. |