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Incentive Compatible Privacy-Preserving Distributed Classification

机译:激励兼容的隐私保护分布式分类

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In this paper, we propose game-theoretic mechanisms to encourage truthful data sharing for distributed data mining. One proposed mechanism uses the classic Vickrey-Clarke-Groves (VCG) mechanism, and the other relies on the Shapley value. Neither relies on the ability to verify the data of the parties participating in the distributed data mining protocol. Instead, we incentivize truth telling based solely on the data mining result. This is especially useful for situations where privacy concerns prevent verification of the data. Under reasonable assumptions, we prove that these mechanisms are incentive compatible for distributed data mining. In addition, through extensive experimentation, we show that they are applicable in practice.
机译:在本文中,我们提出了博弈论机制来鼓励真实数据共享以进行分布式数据挖掘。一种建议的机制使用经典的Vickrey-Clarke-Groves(VCG)机制,另一种则依赖于Shapley值。两者都不依赖于验证参与分布式数据挖掘协议的各方的数据的能力。相反,我们仅根据数据挖掘结果来激励说真话。这在隐私问题阻止数据验证的情况下特别有用。在合理的假设下,我们证明了这些机制对于分布式数据挖掘具有激励兼容性。此外,通过广泛的实验,我们证明它们在实践中是适用的。

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