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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
Template:Anchor
| Name
|
Organization
|
Year Introduced
|
Notes
|
Still in use?
|
| OnTheMap: Interactive tool for exploration of US income and commute patterns.[1][2]
|
US Census Bureau
|
2008
|
First deployment of differential privacy
|
Yes
|
| RAPPOR in Chrome Browser to collect security metrics[3][4]
|
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)[5]
|
Apple
|
2017
|
|
Yes
|
| Application telemetry[6]
|
Microsoft
|
2017
|
Application usage statistics Microsoft Windows.
|
yes
|
| Flex: A SQL-based system developed for internal Uber analytics[7][8]
|
Uber
|
2017
|
|
Unknown
|
| 2020 Census[9]
|
US Census Bureau
|
2018
|
|
Yes
|
| Audience Engagement API[10]
|
LinkedIn
|
2020
|
|
Yes
|
| Labor Market Insights[11]
|
LinkedIn
|
2020
|
|
Yes
|
| COVID-19 Community Mobility Reports[12]
|
Google
|
2020
|
|
Unknown
|
| Advertiser Queries[13]
|
LinkedIn
|
2020
|
|
| U.S. Broadband Coverage Data Set[14]
|
Microsoft
|
2021
|
|
Unknown
|
| College Scorecard Website
|
IRS and Dept. of Education
|
2021
|
|
Unknown
|
| Ohm Connect[15]
|
Recurve
|
2021
|
|
|
| Live Birth Dataset[16][17]
|
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[18]
|
Google
|
2019
|
Building block libraries in Go, C++, and Java; end-to-end framework in Go,.[19]
|
Yes
|
| OpenDP[20]
|
Harvard, Microsoft
|
2020
|
Core library in Rust,[21] SDK in Python with an SQL interface.
|
Yes
|
| Tumult Analytics[22]
|
Tumult Labs[23]
|
2022
|
Python library, running on Apache Spark.
|
Yes
|
| PipelineDP[24]
|
Google, OpenMined[25]
|
2022
|
Python library, running on Apache Spark, Apache Beam, or locally.
|
Yes
|
| PSI (Ψ): A Private data Sharing Interface
|
Harvard University Privacy Tools Project.[26]
|
2016
|
|
No
|
| TopDown Algorithm[27]
|
United States Census Bureau
|
2020
|
Production code used in the 2020 US Census.
|
No
|
Machine learning
| Name
|
Developer
|
Year Introduced
|
Notes
|
Still maintained?
|
| Diffprivlib[28]
|
IBM[29]
|
2019
|
Python library.
|
Yes
|
| TensorFlow Privacy[30][31]
|
Google
|
2019
|
Differentially private training in TensorFlow.
|
Yes
|
| Opacus[32]
|
Meta
|
2020
|
Differentially private training in PyTorch.
|
Yes
|
Research projects and prototypes
| Name
|
Citation
|
Year Published
|
Notes
|
| PINQ: An API implemented in C#.
|
[33]
|
2010
|
|
| Airavat: A MapReduce-based system implemented in Java hardened with SELinux-like access control.
|
[34]
|
2010
|
|
| Fuzz: Time-constant implementation in Caml Light of a domain-specific language.
|
[35]
|
2011
|
|
| GUPT: Implementation of the sample-and-aggregate framework.
|
[36]
|
2012
|
|
| KTELO: A framework and system for answering linear counting queries.
|
[37]
|
2018
|
|
See also
References