As a method for answer aggregation in crowdsourced data management, rank aggregation aims to combine different agents' answers or preferences over the given alternatives into an aggregate ranking which agrees the most with the preferences. However, since the aggregation procedure relies on a data curator, the privacy within the agents' preference data could be compromised when the curator is untrusted. Existing works that guarantee differential privacy in rank aggregation all assume that the data curator is trusted. In this paper, we formulate and address the problem of locally differentially private rank aggregation, in which the agents have no trust in the data curator. By leveraging the approximate rank aggregation algorithm KwikSort, the Randomized Response mechanism, and the Laplace mechanism, we propose an effective and efficient protocol LDP-KwikSort. Theoretical and empirical results show that the solution LDP-KwikSort:RR can achieve the acceptable trade-off between the utility of aggregate ranking and the privacy protection of agents' pairwise preferences.

中文翻译：

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局部差分隐私下的私有等级聚合

作为众包数据管理中答案聚合的一种方法，排名聚合旨在将不同代理的答案或对给定替代方案的偏好组合成与偏好最一致的聚合排名。然而，由于聚合过程依赖于数据管理者，当管理者不受信任时，代理偏好数据中的隐私可能会受到损害。现有的在等级聚合中保证差异隐私的工作都假设数据管理者是可信的。在本文中，我们制定并解决了局部差异私有等级聚合的问题，其中代理不信任数据管理者。通过利用近似秩聚合算法 KwikSort、随机响应机制和拉普拉斯机制，我们提出了一种有效且高效的协议 LDP-KwikSort。理论和实证结果表明，解决方案 LDP-KwikSort:RR 可以在聚合排名的效用和代理成对偏好的隐私保护之间实现可接受的权衡。