Users worldwide access massive amounts of curated data in the form of rankings on a daily basis. The societal impact of this ease of access has been studied and work has been done to propose and enforce various notions of fairness in rankings. Current computational methods for fair item ranking rely on disclosing user data to a centralized server, which gives rise to privacy concerns for the users. This work is the first to advance research at the conjunction of producer (item) fairness and consumer (user) privacy in rankings by exploring the incorporation of privacy-preserving techniques; specifically, differential privacy and secure multi-party computation. Our work extends the equity of amortized attention ranking mechanism to be privacy-preserving, and we evaluate its effects with respect to privacy, fairness, and ranking quality. Our results using real-world datasets show that we are able to effectively preserve the privacy of users and mitigate unfairness of items without making additional sacrifices to the quality of rankings in comparison to the ranking mechanism in the clear.

翻译：全球用户每天以排序列表的形式访问海量精选数据。这种便捷访问带来的社会影响已被广泛研究，研究者们提出并实施了多种排序中的公平性概念。当前公平项目排序的计算方法依赖将用户数据披露给中心化服务器，这引发了用户的隐私担忧。本文首次探索将隐私保护技术——具体包括差分隐私和安全多方计算——融入排序中的生产者（项目）公平性与消费者（用户）隐私性交叉领域。我们将均摊注意力排序机制的公平性扩展至隐私保护场景，并从隐私、公平性和排序质量三个维度评估其效果。在真实数据集上的实验结果表明，与明文排序机制相比，本方法能在不额外牺牲排序质量的前提下，有效保护用户隐私并缓解项目不公平性。