  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.
