mlsanityData to 7 Oct 2026

When Differential Privacy Meets Interpretability: A Case Study

arXiv 2106.13203 ↗First posted 24 Jun 2021cs.CV cs.CRcited by 8

Not on the map. Why?

Abstract (arXiv)

Given the increase in the use of personal data for training Deep Neural Networks (DNNs) in tasks such as medical imaging and diagnosis, differentially private training of DNNs is surging in importance and there is a large body of work focusing on providing better privacy-utility trade-off. However, little attention is given to the interpretability of these models, and how the application of DP affects the quality of interpretations. We propose an extensive study into the effects of DP training on DNNs, especially on medical imaging applications, on the APTOS dataset.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 24 Jun 2021.

Builds on

14 of 19 references.

References · 14

  1. 2016Deep Learning with Differential Privacycited by 2,682
  2. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  3. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 936
  4. 2020Differentially Private Learning Needs Better Features (or Much More Data)cited by 233
  5. 2018Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoisingcited by 216
  6. 2020Privacy in Deep Learning: A Surveycited by 70
  7. 2018Recent progress in semantic image segmentationcited by 39
  8. 2020Chasing Your Long Tails: Differentially Private Prediction in Health Care Settingscited by 37
  9. 2020Model Explanations with Differential Privacycited by 23
  10. 2019Advancements in Image Classification using Convolutional Neural Networkcited by 20
  11. 2019Local Differential Privacy: a tutorialcited by 13
  12. 2021U-Noise: Learnable Noise Masks for Interpretable Image Segmentationcited by 9
2 more
  1. 2020Benchmarking Differentially Private Residual Networks for Medical Imagerycited by 3
  2. 2021FedPandemic: A Cross-Device Federated Learning Approach Towards Elementary Prognosis of Diseases During a Pandemiccited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 3; 2025: 0; 2026: 1.

8 papers.

  1. 2022Privacy Meets Explainability: A Comprehensive Impact Benchmarkcited by 9
  2. 2024A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasurescited by 6
  3. 2023SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learningcited by 4
  4. 2024Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainabilitycited by 2
  5. 2023Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Modelscited by 1
  6. 2026Explanations Leak: Membership Inference with Differential Privacy and Active Learning Defensecited by 1
  7. 2022Tensions Between the Proxies of Human Values in AIcited by 0
  8. 2024Which PPML Would a User Choose? A Structured Decision Support Framework for Developers to Rank PPML Techniques Based on User Acceptance Criteriacited by 0