# List of implementations of differentially private analyses

Since the advent of differential privacy, a number of systems supporting differentially private data analyses have been implemented and deployed. This article tracks real-world deployments, production software packages, and research prototypes.

## Real-world deployments

| Name | Organization | Year Introduced | Notes | Still in use? |
| OnTheMap: Interactive tool for exploration of US income and commute patterns. | US Census Bureau | 2008 | First deployment of differential privacy | Yes |
| RAPPOR in Chrome Browser to collect security metrics | Google | 2014 | First widespread use of local differential privacy | No |
| Emoji analytics; analytics. Improve: QuickType, emoji; Spotlight deep link suggestions; Lookup Hints in Notes. Emoji suggestions, health type usage estimates, Safari energy drain statistics, Autoplay intent detection (also in Safari) | Apple | 2017 | | Yes |
| Application telemetry | Microsoft | 2017 | Application usage statistics Microsoft Windows. | yes |
| Flex: A SQL-based system developed for internal Uber analytics | Uber | 2017 | | Unknown |
| 2020 Census | US Census Bureau | 2018 | | Yes |
| Audience Engagement API | LinkedIn | 2020 | | Yes |
| Labor Market Insights | LinkedIn | 2020 | | Yes |
| COVID-19 Community Mobility Reports | Google | 2020 | | Unknown |
| Advertiser Queries | LinkedIn | 2020 | |
| U.S. Broadband Coverage Data Set | Microsoft | 2021 | | Unknown |
| College Scorecard Website | IRS and Dept. of Education | 2021 | | Unknown |
| Ohm Connect | Recurve | 2021 | | |
| Live Birth Dataset | Israeli Ministry of Health | 2024 | | Yes |

## Production software packages

These software packages purport to be usable in production systems. They are split in two categories: those focused on answering statistical queries with differential privacy, and those focused on training machine learning models with differential privacy.

### Statistical analyses

| Name | Developer | Year Introduced | Notes | Still maintained? |
| Google's differential privacy libraries | Google | 2019 | Building block libraries in Go, C++, and Java; end-to-end framework in Go,. | Yes |
| OpenDP | Harvard, Microsoft | 2020 | Core library in Rust, SDK in Python with an SQL interface. | Yes |
| Tumult Analytics | Tumult Labs | 2022 | Python library, running on Apache Spark. | Yes |
| PipelineDP | Google, OpenMined | 2022 | Python library, running on Apache Spark, Apache Beam, or locally. | Yes |
| PSI (Ψ): A Private data Sharing Interface | Harvard University Privacy Tools Project. | 2016 | | No |
| TopDown Algorithm | United States Census Bureau | 2020 | Production code used in the 2020 US Census. | No |

### Machine learning

| Name | Developer | Year Introduced | Notes | Still maintained? |
| Diffprivlib | IBM | 2019 | Python library. | Yes |
| TensorFlow Privacy | Google | 2019 | Differentially private training in TensorFlow. | Yes |
| Opacus | Meta | 2020 | Differentially private training in PyTorch. | Yes |

## Research projects and prototypes

| Name | Year Published |
| PINQ: An API implemented in C#. | 2010 |
| Airavat: A MapReduce-based system implemented in Java hardened with SELinux-like access control. | 2010 |
| Fuzz: Time-constant implementation in Caml Light of a domain-specific language. | 2011 |
| GUPT: Implementation of the sample-and-aggregate framework. | 2012 |
| $\epsilon$KTELO: A framework and system for answering linear counting queries. | 2018 |