Private Rank Aggregation in Central and Local Models
This work addresses privacy concerns in social choice applications, providing theoretical guarantees for aggregating rankings without revealing individual preferences.
The paper tackles the problem of rank aggregation while preserving voter privacy, presenting differentially private algorithms for both central and local models with distribution-independent utility bounds.
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.