mlsanityData to 7 Oct 2026

SoK: Differential Privacies

arXiv 1906.01337 ↗First posted 4 Jun 2019cs.CRcited by 33

Not on the map. Why?

Abstract (arXiv)

Shortly after it was first introduced in 2006, differential privacy became the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt it to different scenarios and attacker models. In this work, we propose a systematic taxonomy of these variants and extensions. We list all data privacy definitions based on differential privacy, and partition them into seven categories, depending on which aspect of the original definition is modified. These categories act like dimensions: variants from the same category cannot be combined, but variants from different categories can be combined to form new definitions. We also establish a partial ordering of relative strength between these notions by summarizing existing results. Furthermore, we list which of these definitions satisfy some desirable properties, like composition, post-processing, and convexity by either providing a novel proof or collecting existing ones.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jun 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 6; 2023: 6; 2024: 5; 2025: 7; 2026: 4.

33 papers.

Sort:

By area:

  1. 2021Machine Unlearning of Features and Labelscited by 223
  2. 2022SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learningcited by 47
  3. 2021Formalizing and Estimating Distribution Inference Riskscited by 42
  4. 2024Private Fine-tuning of Large Language Models with Zeroth-order Optimizationcited by 41
  5. 2022DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewritingcited by 28
  6. 2021When differential privacy meets NLP: The devil is in the detailcited by 25
  7. 2023Differentially Private Natural Language Models: Recent Advances and Future Directionscited by 20
  8. 2021Privacy-Preserving Graph Convolutional Networks for Text Classificationcited by 19
  9. 2022Distribution inference risks: Identifying and mitigating sources of leakagecited by 17
  10. 2023Flow-based Distributionally Robust Optimizationcited by 13
  11. 2022Differential Privacy in Natural Language Processing: The Story So Farcited by 12
  12. 2023Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Surveycited by 12
21 more
  1. 2024A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theoremcited by 8
  2. 2025On the Impact of Noise in Differentially Private Text Rewritingcited by 8
  3. 2021Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descentcited by 5
  4. 2023Differential Privacy with Random Projections and Sign Random Projectionscited by 5
  5. 2022Collaborative Drug Discovery: Inference-level Data Protection Perspectivecited by 4
  6. 2022Algorithms with More Granular Differential Privacy Guaranteescited by 4
  7. 2025The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditingcited by 4
  8. 2023WW-FL: Secure and Private Large-Scale Federated Learningcited by 2
  9. 2023Differential Privacy via Distributionally Robust Optimizationcited by 2
  10. 2024Auditing Privacy Mechanisms via Label Inference Attackscited by 2
  11. 2025Managing Correlations in Data and Privacy Demandcited by 2
  12. 2024Bridging Privacy and Robustness for Trustworthy Machine Learningcited by 1
  13. 2024Making Old Things New: A Unified Algorithm for Differentially Private Clusteringcited by 1
  14. 2025Quantitative Auditing of AI Fairness with Differentially Private Synthetic Datacited by 1
  15. 2025Detect & Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learningcited by 1
  16. 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspectivecited by 1
  17. 2025Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)cited by 0
  18. 2025Rao Differential Privacycited by 0
  19. 2026Bayesian Adversarial Privacycited by 0
  20. 2026On Choosing the μ Parameter in Gaussian Differential Privacycited by 0
  21. 2026DP-IPI: A Hybrid Differential Privacy Text Rewriting Mechanism for Indirect Personal Identifiers in Clinical Textscited by 0