mlsanityData to 7 Oct 2026

Regression with Label Differential Privacy

arXiv 2212.06074 ↗First posted 12 Dec 2022cs.LG cs.CRcited by 14

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Abstract (arXiv)

We study the task of training regression models with the guarantee of label differential privacy (DP). Based on a global prior distribution on label values, which could be obtained privately, we derive a label DP randomization mechanism that is optimal under a given regression loss function. We prove that the optimal mechanism takes the form of a "randomized response on bins", and propose an efficient algorithm for finding the optimal bin values. We carry out a thorough experimental evaluation on several datasets demonstrating the efficacy of our algorithm.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 16 of its 42 references that are in the map. First posted on arXiv 12 Dec 2022.

Builds on

16 of 42 references.

References · 16

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2016Deep Learning with Differential Privacycited by 2,682
  5. 2021Opacus: User-Friendly Differential Privacy Library in PyTorchcited by 375
  6. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 347
  7. 2018Scalable Private Learning with PATEcited by 266
  8. 2021Deep Learning with Label Differential Privacycited by 113
  9. 2021Label Leakage and Protection in Two-party Split Learningcited by 87
  10. 2014Private Learning and Sanitization: Pure vs. Approximate Differential Privacycited by 62
  11. 2021Antipodes of Label Differential Privacy: PATE and ALIBIcited by 50
  12. 2019Data Poisoning Attacks to Local Differential Privacy Protocolscited by 20
4 more
  1. 2021Label differential privacy via clusteringcited by 20
  2. 2018Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Searchcited by 18
  3. 2021Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Messagecited by 18
  4. 2021Handling many conversions per click in modeling delayed feedbackcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 1; 2026: 4.

14 papers.

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  1. 2023Private Learning with Public Featurescited by 6
  2. 2023Optimal Unbiased Randomizers for Regression with Label Differential Privacycited by 6
  3. 2024Exactly Minimax-Optimal Locally Differentially Private Samplingcited by 5
  4. 2024Retraining with Predicted Hard Labels Provably Increases Model Accuracycited by 4
  5. 2024Locally Private Estimation with Public Featurescited by 3
  6. 2024Enhancing Learning with Label Differential Privacy by Vector Approximationcited by 2
  7. 2023Label Differential Privacy via Aggregationcited by 1
  8. 2024On Convex Optimization with Semi-Sensitive Featurescited by 1
  9. 2026RPWithPrior: Label Differential Privacy in Regressioncited by 1
  10. 2024Aggregating Data for Optimal and Private Learningcited by 0
  11. 2025On Theoretical Limits of Learning with Label Differential Privacycited by 0
  12. 2026BlockRR: A Unified Framework of RR-type Algorithms for Label Differential Privacycited by 0
2 more
  1. 2026Private Learning with Public Feature Conditioningcited by 0
  2. 2026End-to-End Differential Privacy in Training Deep Neural Network Classifierscited by 0