# Diffprivlib: The IBM Differential Privacy Library

arXiv: https://arxiv.org/abs/1907.02444 · HTML: https://mlsanity.com/paper/1907.02444/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2019.

## Built on by (most cited first, 30 of 53)

- [Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data](https://mlsanity.com/paper/2109.11429/) (2021)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [A General Framework for Auditing Differentially Private Machine Learning](https://mlsanity.com/paper/2210.08643/) (2022)
- [Accuracy, Interpretability, and Differential Privacy via Explainable Boosting](https://mlsanity.com/paper/2106.09680/) (2021)
- [Arbitrary Decisions are a Hidden Cost of Differentially Private Training](https://mlsanity.com/paper/2302.14517/) (2023)
- [How unfair is private learning ?](https://mlsanity.com/paper/2206.03985/) (2022)
- [Private Distribution Learning with Public Data: The View from Sample Compression](https://mlsanity.com/paper/2308.06239/) (2023)
- [dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generation](https://mlsanity.com/paper/2207.05810/) (2022)
- [DP-TabICL: In-Context Learning with Differentially Private Tabular Data](https://mlsanity.com/paper/2403.05681/) (2024)
- [Post-processing Private Synthetic Data for Improving Utility on Selected Measures](https://mlsanity.com/paper/2305.15538/) (2023)
- [DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning](https://mlsanity.com/paper/2112.12998/) (2021)
- [Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data](https://mlsanity.com/paper/2310.19250/) (2023)
- [Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations](https://mlsanity.com/paper/2310.19978/) (2023)
- [Catch-Only-One: Non-Transferable Examples for Model-Specific Authorization](https://mlsanity.com/paper/2510.10982/) (2025)
- [Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage](https://mlsanity.com/paper/2308.04341/) (2023)
- [Privacy-Preserving Algorithmic Recourse](https://mlsanity.com/paper/2311.14137/) (2023)
- [The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data](https://mlsanity.com/paper/2504.06923/) (2025)
- [Classification with Partially Private Features](https://mlsanity.com/paper/2312.07583/) (2023)
- [Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference](https://mlsanity.com/paper/2510.18768/) (2025)
- [DP-XGBoost: Private Machine Learning at Scale](https://mlsanity.com/paper/2110.12770/) (2021)
- [Exploring the Unfairness of DP-SGD Across Settings](https://mlsanity.com/paper/2202.12058/) (2022)
- [Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile](https://mlsanity.com/paper/2306.15790/) (2023)
- [An automatic differentiation system for the age of differential privacy](https://mlsanity.com/paper/2109.10573/) (2021)
- [Private Boosted Decision Trees via Smooth Re-Weighting](https://mlsanity.com/paper/2201.12648/) (2022)
- [Spending Privacy Budget Fairly and Wisely](https://mlsanity.com/paper/2204.12903/) (2022)
- [FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacy](https://mlsanity.com/paper/2405.02437/) (2024)
- [Differentially Private Synthetic Data via APIs 4: Tabular Data](https://mlsanity.com/paper/2606.08259/) (2026)
- [Clustering Algorithm to Detect Adversaries in Federated Learning](https://mlsanity.com/paper/2102.10799/) (2021)
- [Statistical Privacy Guarantees of Machine Learning Preprocessing Techniques](https://mlsanity.com/paper/2109.02496/) (2021)
- [Differentially-Private Decision Trees and Provable Robustness to Data Poisoning](https://mlsanity.com/paper/2305.15394/) (2023)

## 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.