mlsanityData to 7 Oct 2026

The Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with Exponential Noise

arXiv 2105.07260 ↗First posted 15 May 2021cs.CRcited by 14

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Abstract (arXiv)

The permute-and-flip mechanism is a recently proposed differentially private selection algorithm that was shown to outperform the exponential mechanism. In this paper, we show that permute-and-flip is equivalent to the well-known report noisy max algorithm with exponential noise.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 May 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 3; 2025: 3; 2026: 2.

14 papers.

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  1. 2023Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generationcited by 94
  2. 2024Permute-and-Flip: An optimally stable and watermarkable decoder for LLMscited by 17
  3. 2022Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATEcited by 9
  4. 2023Unbounded Differentially Private Quantile and Maximum Estimationcited by 6
  5. 2022Differentially Private Top-k Selection via Canonical Lipschitz Mechanismcited by 2
  6. 2023Randomized algorithms for precise measurement of differentially-private, personalized recommendationscited by 1
  7. 2024Faster Private Minimum Spanning Treescited by 1
  8. 2025Differentially Private and Federated Structure Learning in Bayesian Networkscited by 1
  9. 2023Differentially Private Conditional Independence Testingcited by 0
  10. 2024Unleash the Power of Ellipsis: Accuracy-enhanced Sparse Vector Technique with Exponential Noisecited by 0
  11. 2025Private Selection with Heterogeneous Sensitivitiescited by 0
  12. 2025Differentially Private Selection using Smooth Sensitivitycited by 0
2 more
  1. 2026DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacycited by 0
  2. 2026ExpBoN: Exponential-Noise Best-of-n for Efficient Test-Time LLM Alignmentcited by 0