Private Rank Aggregation in Central and Local Models
Abstract
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.
Cite
Text
Alabi et al. "Private Rank Aggregation in Central and Local Models." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I6.20544Markdown
[Alabi et al. "Private Rank Aggregation in Central and Local Models." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/alabi2022aaai-private/) doi:10.1609/AAAI.V36I6.20544BibTeX
@inproceedings{alabi2022aaai-private,
title = {{Private Rank Aggregation in Central and Local Models}},
author = {Alabi, Daniel and Ghazi, Badih and Kumar, Ravi and Manurangsi, Pasin},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {5984-5991},
doi = {10.1609/AAAI.V36I6.20544},
url = {https://mlanthology.org/aaai/2022/alabi2022aaai-private/}
}