The Application of Differential Privacy for Rank Aggregation: Privacy and Accuracy
Artificial Intelligence
2014-09-25 v1 Cryptography and Security
Abstract
The potential risk of privacy leakage prevents users from sharing their honest opinions on social platforms. This paper addresses the problem of privacy preservation if the query returns the histogram of rankings. The framework of differential privacy is applied to rank aggregation. The error probability of the aggregated ranking is analyzed as a result of noise added in order to achieve differential privacy. Upper bounds on the error rates for any positional ranking rule are derived under the assumption that profiles are uniformly distributed. Simulation results are provided to validate the probabilistic analysis.
Keywords
Cite
@article{arxiv.1409.6831,
title = {The Application of Differential Privacy for Rank Aggregation: Privacy and Accuracy},
author = {Shang Shang and Tiance Wang and Paul Cuff and Sanjeev Kulkarni},
journal= {arXiv preprint arXiv:1409.6831},
year = {2014}
}
Comments
Fusion 2014