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Diffprivlib: The IBM Differential Privacy Library

arXiv 1907.02444 ↗First posted 4 Jul 2019cs.CR cs.LGcited by 51

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Abstract (arXiv)

Since its conception in 2006, differential privacy has emerged as the de-facto standard in data privacy, owing to its robust mathematical guarantees, generalised applicability and rich body of literature. Over the years, researchers have studied differential privacy and its applicability to an ever-widening field of topics. Mechanisms have been created to optimise the process of achieving differential privacy, for various data types and scenarios. Until this work however, all previous work on differential privacy has been conducted on a ad-hoc basis, without a single, unifying codebase to implement results. In this work, we present the IBM Differential Privacy Library, a general purpose, open source library for investigating, experimenting and developing differential privacy applications in the Python programming language. The library includes a host of mechanisms, the building blocks of differential privacy, alongside a number of applications to machine learning and other data analytics tasks. Simplicity and accessibility has been prioritised in developing the library, making it suitable to a wide audience of users, from those using the library for their first investigations in data privacy, to the privacy experts looking to contribute their own models and mechanisms for others to use.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jul 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 7; 2023: 14; 2024: 5; 2025: 7; 2026: 10.

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  1. 2021Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Datacited by 48
  2. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
  3. 2022A General Framework for Auditing Differentially Private Machine Learningcited by 25
  4. 2021Accuracy, Interpretability, and Differential Privacy via Explainable Boostingcited by 24
  5. 2023Arbitrary Decisions are a Hidden Cost of Differentially Private Trainingcited by 19
  6. 2022How unfair is private learning ?cited by 18
  7. 2023Private Distribution Learning with Public Data: The View from Sample Compressioncited by 14
  8. 2022dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generationcited by 12
  9. 2024DP-TabICL: In-Context Learning with Differentially Private Tabular Datacited by 11
  10. 2023Post-processing Private Synthetic Data for Improving Utility on Selected Measurescited by 9
  11. 2021DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learningcited by 8
  12. 2023Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Datacited by 8
38 more
  1. 2023Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterationscited by 8
  2. 2025Catch-Only-One: Non-Transferable Examples for Model-Specific Authorizationcited by 7
  3. 2023Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakagecited by 5
  4. 2023Privacy-Preserving Algorithmic Recoursecited by 5
  5. 2023Classification with Partially Private Featurescited by 4
  6. 2025The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Datacited by 4
  7. 2025Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inferencecited by 4
  8. 2021DP-XGBoost: Private Machine Learning at Scalecited by 3
  9. 2022Exploring the Unfairness of DP-SGD Across Settingscited by 3
  10. 2023Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profilecited by 3
  11. 2021An automatic differentiation system for the age of differential privacycited by 2
  12. 2022Private Boosted Decision Trees via Smooth Re-Weightingcited by 2
  13. 2022Spending Privacy Budget Fairly and Wiselycited by 2
  14. 2024FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacycited by 2
  15. 2026Differentially Private Synthetic Data via APIs 4: Tabular Datacited by 2
  16. 2021Clustering Algorithm to Detect Adversaries in Federated Learningcited by 1
  17. 2021Statistical Privacy Guarantees of Machine Learning Preprocessing Techniquescited by 1
  18. 2023Differentially-Private Decision Trees and Provable Robustness to Data Poisoningcited by 1
  19. 2023DPM: Clustering Sensitive Data through Separationcited by 1
  20. 2023RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulationcited by 1
  21. 2024Qrlew: Rewriting SQL into Differentially Private SQLcited by 1
  22. 2024Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecastingcited by 1
  23. 2026Differentially Private Preference Data Synthesis for Large Language Model Alignmentcited by 1
  24. 2021Swarm Differential Privacy for Purpose Driven Data-Information-Knowledge-Wisdom Architecturecited by 0
  25. 2023Differentially Private Statistical Inference through β-Divergence One Posterior Samplingcited by 0
  26. 2023DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learningcited by 0
  27. 2024Unified Locational Differential Privacy Frameworkcited by 0
  28. 2025Training Set Reconstruction from Differentially Private Forests: How Effective is DP?cited by 0
  29. 2025Understanding the Impact of Data Domain Extraction on Synthetic Data Privacycited by 0
  30. 2025Representation learning with a transformer by contrastive learning for money laundering detectioncited by 0
  31. 2025PRIVEE: Privacy-Preserving Vertical Federated Learning Against Feature Inference Attackscited by 0
  32. 2026One RNG to Rule Them All: How Randomness Becomes an Attack Vector in Machine Learningcited by 0
  33. 2026Rashomon Sets and Model Multiplicity in Federated Learningcited by 0
  34. 2026On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clusteringcited by 0
  35. 2026Differentially Private Modeling of Disease Transmission within Human Contact Networkscited by 0
  36. 2026Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Treescited by 0
  37. 2026Dithered Gaussian Mechanism for Randomness-Efficient Differential Privacycited by 0
  38. 2026Auditing Fairness-Privacy Trade-offs: Subpopulation-Level Effects of Fairness-Enhancing Algorithmscited by 0

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