The OpenDP team is excited to bring you our latest release, OpenDP Library 0.12! The OpenDP Library is a modular collection of algorithms for building privacy-preserving applications, with an extensible approach to tracking privacy, and a vetted implementation. It is available as binaries for Python on PyPI, for R on R-universe, for Rust on crates.io, or in source…
Applications are now open for the 2025 OpenDP Interns and Visiting Fellows program!
We are pleased to announce the 2025 OpenDP Interns and Visiting Fellows call for applications! Starting today, we are seeking interested individuals that are interested in contributing to and helping grow the OpenDP library and community. OpenDP Interns are typically undergraduate, post-bacc, or graduate students but those at other stages are welcome to apply. It is preferred…
DP Wizard: An Easy Way to Get Started with Differential Privacy and OpenDP
OpenDP is pleased to announce the first release of DP Wizard, an interactive tool to explore the effect of different parameter choices, and an assistant for implementing those choices with the OpenDP library. DP Wizard builds on what we’ve learned from DP Creator, but offers a simplified development and install process and a streamlined workflow. We would…
How OpenDP and PySyft ensured user privacy for the Christchurch Call report
A recent publication of a comprehensive technical report marking the completion of Phase 1 of the Christchurch Call Initiative on Algorithmic Outcomes (CCIAO) was announced last month which highlights advancements in AI Auditing and safety, allowing external researchers to conduct privacy-preserving audits of production recommender algorithms at LinkedIn and Daily Motion using PySyft and OpenDP….
2024 OpenDP Community Meeting – Recap and Survey
The 2024 OpenDP Community Meeting took place from August 22 to 23 at Harvard University. We joined forces with TPDP again this year, transitioning from the conference to the Community Meeting with an evening reception. Attendees reflected on the busy week of TPDP and greeted newcomers over appetizers and drinks in the Science and Engineering…
Recap: 2024 Interns & Fellows Program
This August, we celebrated another successful year of the OpenDP Interns & Fellows program. We hosted three Visiting Fellows: Damien Aymon from the Swiss Federal Statistical Office; Shlomi Hod from Boston University; and Praneeth Vepakomma from MIT and three interns onsite in Boston: Tudor Cebere from Inria; Gurman Dhaliwal from UC San Diego; and Maxine…
Announcing OpenDP Library 0.11
The OpenDP team is excited to bring you our latest release, OpenDP Library 0.11! The OpenDP Library is a modular collection of algorithms for building privacy-preserving applications, with an extensible approach to tracking privacy, and a vetted implementation. It is available as binaries for Python on PyPI, for R on R-universe, for Rust on crates.io, or in source…
Meet the authors of the newly released book, Hands on Differential Privacy!
Meet the co-authors of the newly released book Hands on Differential Privacy in this 10-minute Q&A with Ethan Cowan (Harvard University), Michael Shoemate (OpenDP/Harvard University), and Mayana Pereira (Microsoft). Moderated by Sharon Ayalde (OpenDP/Harvard University), the co-authors shared their thoughts on why they wrote the book, who would find the most value from it, and how it…
Responsible Use of Differential Privacy: Recap and Next Steps
During the 2023 OpenDP Community Meeting, one of the parallel breakout sessions that was held focused on identifying best practices and choosing applications for DP. Named “Responsible Use of Differential Privacy”, the session was led by Alexandra Wood (Harvard University) and Jayshree Sarathy (Columbia University) who guided the group into an interactive session that discussed ethical frameworks for statistical analysis,…
Announcing OpenDP Library 0.10
The OpenDP team is excited to bring you our latest release, OpenDP Library 0.10! The OpenDP Library is a modular collection of algorithms for building privacy-preserving applications, with an extensible approach to tracking privacy, and a vetted implementation. It is available as binaries for Python on PyPI, for R on R-universe, for Rust on crates.io, or in source form…