The Application of Differential Privacy for Rank Aggregation: Privacy and Accuracy
This work addresses privacy concerns for users sharing rankings on social platforms, but it is incremental as it applies existing differential privacy methods to rank aggregation.
The paper tackled the problem of privacy leakage in rank aggregation by applying differential privacy to protect user rankings, deriving upper bounds on error rates for positional ranking rules and validating them through simulations.
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.