  In social choice theory, (Kemeny) rank aggregation is a well-studied problem
where the goal is to combine rankings from multiple voters into a single
ranking on the same set of items. Since rankings can reveal preferences of
voters (which a voter might like to keep private), it is important to aggregate
preferences in such a way to preserve privacy. In this work, we present
differentially private algorithms for rank aggregation in the pure and
approximate settings along with distribution-independent utility upper and
lower bounds. In addition to bounds in the central model, we also present
utility bounds for the local model of differential privacy.

Data Structures and Algorithms;Cryptography and Security;Computer Science and Game Theory