论文标题
当地差异隐私符合计算社会选择 - 选民删除下的弹性
Local Differential Privacy Meets Computational Social Choice -- Resilience under Voter Deletion
论文作者
论文摘要
投票系统的弹性一直是计算社会选择中的核心话题。许多投票规则，例如多元化，被证明是脆弱的，因为攻击者可以针对特定的选民来操纵结果。如果采用当地的差异隐私（LDP）机制，以使选民在选举前民意调查中从未揭示对选民的真正偏好怎么办？在这种情况下，攻击者只能推断出有关选民真正偏好的随机信息，这可能会导致对选举结果的操纵更加困难。本文的目的是提供一项关于采用南股自由顾展机制对投票系统的影响的定量研究。我们介绍了公制的POLDP（LDP的功率），该公制在LDP机制下定量测量了攻击者的操纵成本与没有LDP机制之间的差异。 POLDP越大，LDP机制的鲁棒性可以添加到投票系统中。我们为POLDP提供了具有多元性规则的投票系统的全面表征，并为使用最不发达国家的机制提供了一般指导。
The resilience of a voting system has been a central topic in computational social choice. Many voting rules, like plurality, are shown to be vulnerable as the attacker can target specific voters to manipulate the result. What if a local differential privacy (LDP) mechanism is adopted such that the true preference of a voter is never revealed in pre-election polls? In this case, the attacker can only infer stochastic information about a voter's true preference, and this may cause the manipulation of the electoral result significantly harder. The goal of this paper is to provide a quantitative study on the effect of adopting LDP mechanisms on a voting system. We introduce the metric PoLDP (power of LDP) that quantitatively measures the difference between the attacker's manipulation cost under LDP mechanisms and that without LDP mechanisms. The larger PoLDP is, the more robustness LDP mechanisms can add to a voting system. We give a full characterization of PoLDP for the voting system with plurality rule and provide general guidance towards the application of LDP mechanisms.
