Privacy Enhancing Technologies (PET) Initiative
The Linux Foundation Privacy Enhancing Technologies (LF PET) Initiative is a community-driven initiative dedicated to the development and support of open-source privacy-enhancing technologies. Privacy-enhancing technologies, or PETs, are tools and techniques designed to minimize personal data use and maximize data security while empowering individuals. By bringing together industry, academic, and open source contributors, the LF PET Initiative aims to foster collaboration and innovation in PET solutions (such as secure multi-party computation, federated learning, homomorphic encryption, confidential computing, and other privacy-preserving methods). The goal is to make advanced PETs broadly accessible and interoperable. As one Linux Foundation leader observed, PETs like secure MPC are “critical for building trust across data-driven industries” and preserving values of data protection. Through open governance and shared resources, LF PET helps ensure these technologies are developed in a transparent and sustainable way.
Mission and Goals
- Enable open collaboration. Build a neutral home where experts and organizations can work together on PET research and implementations.
- Advance practical PET solutions. Encourage development of production-ready tools and frameworks for real-world use cases, from healthcare and finance to advertising.
- Foster user trust and protect privacy. Help organizations analyze data safely with tools that keep personal information private, demonstrating a commitment to responsible data practices.
- Ensure accessibility and transparency. Adopt open source practices so that PETs are “accessible, transparent, and globally impactful,” as emphasized by Linux Foundation leaders.
- Educate and grow the community. Share best practices, reference implementations, and educational resources so that PET adoption can accelerate across industries. The long-term vision of LF PET is a thriving ecosystem of interoperable PET tools and standards. The initiative seeks to unify and expand the PET community, drive interoperability between different privacy technologies (e.g. combining federated learning with TEEs and MPC), and ultimately make privacy-preserving data analysis a common practice.
Governance
LF PET’s governance framework is being established through key charter and agreement documents. These outline the project’s mission, scope, and processes. Current draft documents include:
- Technical Charter (Draft): Privacy Enhancing Tech Technical Charter (defines project scope and governance structure) https://docs.google.com/open?id=11fBWEuXaoclo5EtK8_ZWtY9JVB_ISA31v66HEKtyubU
For your nformation, there is no need to sign the following documents:
- Contribution Agreement (Draft): Privacy Enhancing Tech Contribution Agreement (standard Linux Foundation contributor license terms) https://docs.google.com/open?id=192RPZ__crOh7L9o4OgEoZgZjUPyD1tnD47QMyFRI4Mw
- Series Agreement (Draft): Privacy Enhancing Tech Series Agreement (for collaborative series of projects, if applicable) https://docs.google.com/document/d/1iIc1wDPuClA09Hk807B1-Wy-FSN7H4MEE6JvTo1SIF0/edit?tab=t.0 These living documents guide how the project is managed and how community members can participate. Once finalized, they will be published on the LF PET site and linked from this README.
Community Collaboration
Community involvement is central to LF PET. We maintain a public mailing list for ongoing discussion and design proposals. To join the conversation, subscribe to the technical-discuss@lists.privacyenhancingtech.org mailing list here: https://lists.privacyenhancingtech.org/g/technical-discuss/. This forum is the primary place for announcements, design discussions, and community Q&A. New contributors are encouraged to introduce themselves on the list, ask questions, and propose new ideas. In addition, we welcome participation via GitHub. Use the issue tracker to report bugs or suggest features, and submit pull requests on the lfpet/pet GitHub repository. Be sure to review the project’s Code of Conduct and Contribution Guidelines (see the CONTRIBUTING.md file) before contributing. We strive for an open, respectful community where any contributor can help improve the technology.
Meetings
The LF PET project holds bi-weekly technical meetings to coordinate work and discuss new proposals. These virtual meetings occur every other Thursday at 10:00 AM Pacific Time (PT). All interested parties are welcome to attend. Meeting agendas, slides, and notes are published on the project Wiki. See the Technical Meetings page on the GitHub Wiki for links to past agendas and minutes. For example, meeting summaries and recordings can be found in the project wiki under Technical Meetings. We post calendar invites on our mailing list; join the list to receive notifications of upcoming meetings.
Contribution Guidelines
Contributions of all kinds are welcome! Whether you are a developer, researcher, or user of PETs, you can help the project in many ways:
- Report and discuss bugs or missing features via GitHub issues.
- Propose or submit code improvements and new components through pull requests.
- Help with documentation, examples, and tutorials (e.g., editing the Wiki or README).
- Participate in working groups or special interest subprojects (e.g., focused on MPC, federated learning, TEEs, etc.).
- Provide feedback on design proposals posted to the mailing list. Before contributing code, you will need to sign the Linux Foundation Contributor License Agreement (CLA) as described in our repository. All contributions should follow the project’s coding and review guidelines. See the CONTRIBUTING.md file in the repository for details. We also adhere to a Code of Conduct to ensure the community remains welcoming and inclusive. Open collaboration is what makes LF projects succeed. Your participation – big or small – helps improve privacy technologies that benefit everyone. As one contributor put it in a related Linux Foundation project, “Donating [the code] to Linux Foundation is the natural next step—enabling us to collaborate with an incredible network of partners to take this vision further.”
License
This project is released as open source software under the Apache License 2.0. This permissive license (used widely by Linux Foundation projects) allows broad use, modification, and distribution of the code. For example, the TikTok Privacy Innovation team’s PrivacyGo Data Clean Room project also uses Apache-2.0. By using Apache 2.0, LF PET ensures contributors and users have clear rights to apply these privacy-preserving tools in any domain.
Additional Resources
For more information and detailed documentation, see the project’s online resources:
- GitHub Wiki: Visit the LF PET GitHub Wiki for design documents, API references, meeting notes, and other technical resources. Home
- Technical Meetings: The Technical Meetings wiki page archives all meeting agendas and minutes for historical reference. - LF PET Technical Meetings
- Linux Foundation PET Overview: We will publish an overview of the PET project on the Linux Foundation website and community pages (watch for updates on linuxfoundation.org).
These resources are regularly updated by the community. Stay tuned to the mailing list and Wiki for the latest developments.
References: The above information is drawn from the Linux Foundation’s community guidelines and related projects. For background on PETs, see Privacy-Enhancing Technologies (PETs) are defined as solutions that “minimize personal data use, maximize data security, and empower individuals”. The Linux Foundation emphasizes that privacy-preserving tech is foundational for trust and innovation. Together, LF PET’s governance, communications channels, and open approach reflect these principles.
Wiki