SoK: Differential Privacies
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
Citing papers per year: 2021: 5; 2022: 6; 2023: 6; 2024: 5; 2025: 7; 2026: 4.
33 papers.
Sort:
By area:
- 2021Machine Unlearning of Features and Labelscited by 223
- 2022SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learningcited by 47
- 2021Formalizing and Estimating Distribution Inference Riskscited by 42
- 2024Private Fine-tuning of Large Language Models with Zeroth-order Optimizationcited by 41
- 2022DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewritingcited by 28
- 2021When differential privacy meets NLP: The devil is in the detailcited by 25
- 2023Differentially Private Natural Language Models: Recent Advances and Future Directionscited by 20
- 2021Privacy-Preserving Graph Convolutional Networks for Text Classificationcited by 19
- 2022Distribution inference risks: Identifying and mitigating sources of leakagecited by 17
- 2023Flow-based Distributionally Robust Optimizationcited by 13
- 2022Differential Privacy in Natural Language Processing: The Story So Farcited by 12
- 2023Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Surveycited by 12
21 more
- 2024A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theoremcited by 8
- 2025On the Impact of Noise in Differentially Private Text Rewritingcited by 8
- 2021Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descentcited by 5
- 2023Differential Privacy with Random Projections and Sign Random Projectionscited by 5
- 2022Collaborative Drug Discovery: Inference-level Data Protection Perspectivecited by 4
- 2022Algorithms with More Granular Differential Privacy Guaranteescited by 4
- 2025The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditingcited by 4
- 2023WW-FL: Secure and Private Large-Scale Federated Learningcited by 2
- 2023Differential Privacy via Distributionally Robust Optimizationcited by 2
- 2024Auditing Privacy Mechanisms via Label Inference Attackscited by 2
- 2025Managing Correlations in Data and Privacy Demandcited by 2
- 2024Bridging Privacy and Robustness for Trustworthy Machine Learningcited by 1
- 2024Making Old Things New: A Unified Algorithm for Differentially Private Clusteringcited by 1
- 2025Quantitative Auditing of AI Fairness with Differentially Private Synthetic Datacited by 1
- 2025Detect & Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learningcited by 1
- 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspectivecited by 1
- 2025Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)cited by 0
- 2025Rao Differential Privacycited by 0
- 2026Bayesian Adversarial Privacycited by 0
- 2026On Choosing the μ Parameter in Gaussian Differential Privacycited by 0
- 2026DP-IPI: A Hybrid Differential Privacy Text Rewriting Mechanism for Indirect Personal Identifiers in Clinical Textscited by 0
- 2026DP-IPI: A Hybrid Differential Privacy Text Rewriting Mechanism for Indirect Personal Identifiers in Clinical Texts31 Aug 2026
- 2026On Choosing the μ Parameter in Gaussian Differential Privacy8 Jun 2026
- 2026Bayesian Adversarial Privacy4 Mar 2026
- 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective30 Jan 2026
- 2025Managing Correlations in Data and Privacy Demand2 Sep 2025
- 2025Rao Differential Privacy23 Aug 2025
- 2025Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)21 Aug 2025
- 2025Detect & Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning30 Jun 2025
- 2025The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing20 Jun 2025
- 2025Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data30 Apr 2025
- 2025On the Impact of Noise in Differentially Private Text Rewriting31 Jan 2025
- 2024A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem14 Sep 2024
21 more
- 2024Making Old Things New: A Unified Algorithm for Differentially Private Clustering17 Jun 2024
- 2024Auditing Privacy Mechanisms via Label Inference Attacks4 Jun 2024
- 2024Bridging Privacy and Robustness for Trustworthy Machine Learning25 Mar 2024
- 2024Private Fine-tuning of Large Language Models with Zeroth-order Optimization9 Jan 2024
- 2023Flow-based Distributionally Robust Optimization30 Oct 2023
- 2023Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey28 Sep 2023
- 2023Differential Privacy with Random Projections and Sign Random Projections22 May 2023
- 2023Differential Privacy via Distributionally Robust Optimization25 Apr 2023
- 2023WW-FL: Secure and Private Large-Scale Federated Learning20 Feb 2023
- 2023Differentially Private Natural Language Models: Recent Advances and Future Directions22 Jan 2023
- 2022SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning21 Dec 2022
- 2022Distribution inference risks: Identifying and mitigating sources of leakage18 Sep 2022
- 2022Algorithms with More Granular Differential Privacy Guarantees8 Sep 2022
- 2022DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting22 Aug 2022
- 2022Differential Privacy in Natural Language Processing: The Story So Far17 Aug 2022
- 2022Collaborative Drug Discovery: Inference-level Data Protection Perspective13 May 2022
- 2021Formalizing and Estimating Distribution Inference Risks13 Sep 2021
- 2021When differential privacy meets NLP: The devil is in the detail7 Sep 2021
- 2021Machine Unlearning of Features and Labels26 Aug 2021
- 2021Privacy-Preserving Graph Convolutional Networks for Text Classification10 Feb 2021
- 2021Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent19 Jan 2021
- 2021Machine Unlearning of Features and Labelscited by 223
- 2021Machine Unlearning of Features and Labels26 Aug 2021
- 2022SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learningcited by 47
- 2021Formalizing and Estimating Distribution Inference Riskscited by 42
- 2024Private Fine-tuning of Large Language Models with Zeroth-order Optimizationcited by 41
- 2022DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewritingcited by 28
- 2021When differential privacy meets NLP: The devil is in the detailcited by 25
- 2023Differentially Private Natural Language Models: Recent Advances and Future Directionscited by 20
- 2021Privacy-Preserving Graph Convolutional Networks for Text Classificationcited by 19
- 2022Distribution inference risks: Identifying and mitigating sources of leakagecited by 17
- 2023Flow-based Distributionally Robust Optimizationcited by 13
- 2022Differential Privacy in Natural Language Processing: The Story So Farcited by 12
- 2023Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Surveycited by 12
- 2024A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theoremcited by 8
12 most cited of 32 All 32 (JSON)
- 2026DP-IPI: A Hybrid Differential Privacy Text Rewriting Mechanism for Indirect Personal Identifiers in Clinical Texts31 Aug 2026
- 2026On Choosing the μ Parameter in Gaussian Differential Privacy8 Jun 2026
- 2026Bayesian Adversarial Privacy4 Mar 2026
- 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective30 Jan 2026
- 2025Managing Correlations in Data and Privacy Demand2 Sep 2025
- 2025Rao Differential Privacy23 Aug 2025
- 2025Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)21 Aug 2025
- 2025Detect & Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning30 Jun 2025
- 2025The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing20 Jun 2025
- 2025Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data30 Apr 2025
- 2025On the Impact of Noise in Differentially Private Text Rewriting31 Jan 2025
- 2024A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem14 Sep 2024
12 newest of 32 All 32 (JSON)