mlsanityData to 7 Oct 2026

Private Prediction Sets

arXiv 2102.06202 ↗First posted 11 Feb 2021cs.LG cs.AI cs.CRcited by 16

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Abstract (arXiv)

In real-world settings involving consequential decision-making, the deployment of machine learning systems generally requires both reliable uncertainty quantification and protection of individuals' privacy. We present a framework that treats these two desiderata jointly. Our framework is based on conformal prediction, a methodology that augments predictive models to return prediction sets that provide uncertainty quantification -- they provably cover the true response with a user-specified probability, such as 90%. One might hope that when used with privately-trained models, conformal prediction would yield privacy guarantees for the resulting prediction sets; unfortunately, this is not the case. To remedy this key problem, we develop a method that takes any pre-trained predictive model and outputs differentially private prediction sets. Our method follows the general approach of split conformal prediction; we use holdout data to calibrate the size of the prediction sets but preserve privacy by using a privatized quantile subroutine. This subroutine compensates for the noise introduced to preserve privacy in order to guarantee correct coverage. We evaluate the method on large-scale computer vision datasets.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 28 of its 64 references that are in the map. First posted on arXiv 11 Feb 2021.

Builds on

28 of 64 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Deep Learning with Differential Privacycited by 2,682
  3. 2016Distribution-Free Predictive Inference For Regressioncited by 657
  4. 2019Conformalized Quantile Regressioncited by 620
  5. 2019Conformal Prediction Under Covariate Shiftcited by 511
  6. 2020Classification with Valid and Adaptive Coveragecited by 390
  7. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 365
  8. 2021Distribution-Free, Risk-Controlling Prediction Setscited by 278
  9. 2016Least Ambiguous Set-Valued Classifiers with Bounded Error Levelscited by 270
  10. 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 262
  11. 2019Predictive inference with the jackknife+cited by 206
  12. 2020Conformal Inference of Counterfactuals and Individual Treatment Effectscited by 150
16 more
  1. 2017Collecting Telemetry Data Privatelycited by 147
  2. 2017Finite Sample Differentially Private Confidence Intervalscited by 103
  3. 2013A Conformal Prediction Approach to Explore Functional Datacited by 85
  4. 2020Robust Validation: Confident Predictions Even When Distributions Shiftcited by 82
  5. 2019Flexible distribution-free conditional predictive bands using density estimatorscited by 64
  6. 2020Moment Multicalibration for Uncertainty Estimationcited by 63
  7. 2019PAC Confidence Sets for Deep Neural Networks via Calibrated Predictioncited by 59
  8. 2021Online Multivalid Learning: Means, Moments, and Prediction Intervalscited by 48
  9. 2019Prediction and outlier detection in classification problemscited by 44
  10. 2015Differentially Private Ordinary Least Squarescited by 36
  11. 2018Cautious Deep Learningcited by 35
  12. 2017Generalization for Adaptively-chosen Estimators via Stable Mediancited by 26
  13. 2019Conformal calibratorscited by 22
  14. 2019Conformal prediction with localizationcited by 22
  15. 2018Locally Private Mean Estimation: Z-test and Tight Confidence Intervalscited by 14
  16. 2018Differentially Private Confidence Intervals for Empirical Risk Minimizationcited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 5; 2023: 3; 2024: 0; 2025: 4; 2026: 2.

16 papers.

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  1. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  2. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 256
  3. 2022Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imagingcited by 107
  4. 2022Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Setscited by 38
  5. 2023Conformal Prediction for Federated Uncertainty Quantification Under Label Shiftcited by 29
  6. 2023One-Shot Federated Conformal Predictioncited by 26
  7. 2022Semantic uncertainty intervals for disentangled latent spacescited by 22
  8. 2023Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Predictioncited by 9
  9. 2022A Closer Look at the Calibration of Differentially Private Learnerscited by 4
  10. 2022Distribution-Free Finite-Sample Guarantees and Split Conformal Predictioncited by 4
  11. 2025Trustworthy AI Must Account for Interactionscited by 4
  12. 2025Privacy-Preserving Conformal Prediction Under Local Differential Privacycited by 2
4 more
  1. 2025Extending Prediction-Powered Inference through Conformal Predictioncited by 1
  2. 2025Differentially Private Conformal Prediction via Quantile Binary Searchcited by 0
  3. 2026Replicable Conformal Predictioncited by 0
  4. 2026Conformal Prediction for Offensive Securitycited by 0