Distributed Differentially Private Ranking Aggregation
This work addresses privacy concerns in cooperative decision-making for scenarios where data curators are untrusted, offering an incremental improvement over existing methods.
The paper tackled the problem of ranking aggregation with an untrustworthy curator by proposing a distributed differentially private mechanism that collects locally private rankings, amplifies privacy via a shuffle model, and aggregates hierarchically, achieving competitive accuracy and privacy protection in experiments.
Ranking aggregation is commonly adopted in cooperative decision-making to assist in combining multiple rankings into a single representative. To protect the actual ranking of each individual, some privacy-preserving strategies, such as differential privacy, are often used. This, however, does not consider the scenario where the curator, who collects all rankings from individuals, is untrustworthy. This paper proposed a mechanism to solve the above situation using the distribute differential privacy framework. The proposed mechanism collects locally differential private rankings from individuals, then randomly permutes pairwise rankings using a shuffle model to further amplify the privacy protection. The final representative is produced by hierarchical rank aggregation. The mechanism was theoretically analysed and experimentally compared against existing methods, and demonstrated competitive results in both the output accuracy and privacy protection.