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Rate-Optimal Rank Aggregation with Private Pairwise Rankings
Motivated from this, we propose a debiased randomized response mechanism to protect the raw pairwise rankings, ensuring consistent estimation of true preferences and rankings in downstream rank aggregation.
Utility Theory of Synthetic Data Generation
The former is defined as the generalization difference between models trained on synthetic and on real data.
Ranking Differential Privacy
This allows us to develop a multistage ranking algorithm to generate synthetic rankings while satisfying the developed $\epsilon$-ranking differential privacy.
On strong convergence of the two-tower model for recommender system
Recommender system is capable of predicting preferred items for a user by integrating information from similar users or items.
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