Social choice provides methods for collective decisions. They include methods for voting and for aggregating rankings. These methods are used in multiagent systems for similar purposes when decisions are to be made by agents. Votes and rankings are sensitive information. Because of that, privacy mechanisms are needed to avoid the disclosure of sensitive information. Cryptographic techniques can be applied in centralized environments to avoid the disclosure of sensitive information. A trusted third party can then compute the outcome. In distributed environments, we can use a secure multiparty computation approach for implementing a collective decision method. Other privacy models exist. Differential privacy and k-anonymity are two of them. They provide privacy guarantees that are complementary to multiparty computation approaches, and solutions that can be combined with the cryptographic ones, thus providing additional privacy guarantees, e.g., a differentially private multiparty computation model. In this paper, we propose the use of probabilistic social choice methods to achieve differential privacy. We use the method called random dictatorship and prove that under some circumstances differential privacy is satisfied and propose a variation that is always compliant with this privacy model. Our approach can be implemented using a centralized approach and also a decentralized approach. We briefly discuss these implementations.

中文翻译：

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维护隐私的社会选择的随机专政

社会选择为集体决策提供了方法。它们包括投票和汇总排名的方法。当代理要做出决定时，这些方法在多代理系统中用于类似目的。投票和排名是敏感信息。因此，需要使用隐私机制来避免敏感信息的泄露。加密技术可以应用在集中式环境中，以避免泄露敏感信息。然后，受信任的第三方可以计算结果。在分布式环境中，我们可以使用安全的多方计算方法来实现集体决策方法。存在其他隐私模型。差异隐私和k-匿名是其中两个。它们提供了与多方计算方法互补的隐私保证，以及可以与密码方法相结合的解决方案，从而提供了额外的隐私保证，例如，差分私有多方计算模型。在本文中，我们建议使用概率社会选择方法来实现差异隐私。我们使用一种称为随机独裁的方法，证明在某些情况下可以满足差异性隐私，并提出了一个始终符合此隐私模型的变体。我们的方法可以使用集中式方法也可以使用分散式方法来实施。我们简要讨论这些实现。