Collusion-proof And Sybil-proof Reward Mechanisms For Query Incentive Networks

TL;DR

This paper investigates reward mechanisms for query incentive networks, demonstrating fundamental limitations and proposing two novel mechanisms that enhance resistance to manipulation, with experimental validation showing improved performance over existing methods.

Contribution

The paper introduces two new reward mechanisms that address Sybil and collusion issues in query incentive networks, including one that approximates these properties.

Findings

First mechanism achieves Sybil-proof and collusion-proof properties.

Second mechanism offers approximate Sybil-proof and collusion-proof features.

Experimental results show the second mechanism outperforms existing methods.

Abstract

This paper explores reward mechanisms for a query incentive network in which agents seek information from social networks. In a query tree issued by the task owner, each agent is rewarded by the owner for contributing to the solution, for instance, solving the task or inviting others to solve it. The reward mechanism determines the reward for each agent and motivates all agents to propagate and report their information truthfully. In particular, the reward cannot exceed the budget set by the task owner. However, our impossibility results demonstrate that a reward mechanism cannot simultaneously achieve Sybil-proof (agents benefit from manipulating multiple fake identities), collusion-proof (multiple agents pretend as a single agent to improve the reward), and other essential properties. In order to address these issues, we propose two novel reward mechanisms. The first mechanism achieves Sybil-proof and collusion-proof, respectively; the second mechanism sacrifices Sybil-proof to achieve the approximate versions of Sybil-proof and collusion-proof. Additionally, we show experimentally that our second reward mechanism outperforms the existing ones.