Research map / Federated learning and privacy / Differential privacy
Private Rank Aggregation in Central and Local Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use KwikSort's O(m log m) pairwise queries rather than noising all pairwise entries | Central pure-DP rank aggregation · Kemeny rank aggregation | additive error: \frac{m^4}{\epsilon n} -> \frac{m^3\log m}{\epsilon n} | Helped |
| Use KwikSort's O(m log m) pairwise queries rather than noising all pairwise entries | Central approximate-DP rank aggregation · Kemeny rank aggregation | additive error: \frac{m^3}{\epsilon n}\sqrt{\log \frac{1}{\delta}} -> \frac{m^{2.5}\sqrt{\log m}}{\epsilon n}\sqrt{\log \frac{1}{\delta} | Helped |
| Use KwikSort's O(m log m) pairwise queries rather than estimating all pairwise weights | Local pure-DP rank aggregation · Kemeny rank aggregation | additive error: \frac{m^3}{\epsilon\sqrt{n}} -> \frac{m^{2.5}\log m}{\epsilon\sqrt{n}} | Helped |
| Trade the near-optimal approximation ratio for query-efficient KwikSort | Central pure-DP rank aggregation · Kemeny rank aggregation | approximation ratio: 1 + ξ -> 5 + ξ | Hurt |
| Trade the near-optimal approximation ratio for query-efficient KwikSort | Central approximate-DP rank aggregation · Kemeny rank aggregation | approximation ratio: 1 + ξ -> 5 + ξ | Hurt |
| Trade the near-optimal approximation ratio for query-efficient KwikSort | Local pure-DP rank aggregation · Kemeny rank aggregation | approximation ratio: 1 + ξ -> 5 + ξ | Hurt |
Closest papers by meaning
- Improved Differentially Private Algorithms for Rank Aggregation (AAAI 2026 · similarity 0.91)
- Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message (ICML 2021 · similarity 0.87)
- Selective Preference Aggregation (ICML 2025 · similarity 0.87)
- Fair Ranking with Noisy Protected Attributes (NeurIPS 2022 · similarity 0.87)
- Beyond Pairwise Comparisons in Social Choice: A Setwise Kemeny Aggregation Problem (AAAI 2020 · similarity 0.87)
- Proportional Fairness in Clustering: A Social Choice Perspective (NeurIPS 2024 · similarity 0.85)
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