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https://github.com/curt-mitch/awesome-PETs

A curated list of awesome resources for privacy-enhancing technologies
https://github.com/curt-mitch/awesome-PETs

List: awesome-PETs

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A curated list of awesome resources for privacy-enhancing technologies

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README 

        
# Awesome PETs [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



> A curated list of resources for privacy-enhancing technologies



## Contents



- [General](#general)

- [Differential Privacy](#differential-privacy)

- [Privacy-Preserving Federated Learning](#privacy-preserving-federated-learning)

- [Homomorphic Encryption](#homomorphic-encryption)

- [Secure Multiparty Computation](#secure-multiparty-computation)

- [Synthetic Data](#synthetic-data)

- [Zero-knowledge Proofs and zk-SNARKs](#zero-knowledge-proofs-and-zk-snarks)

- [Privacy-Preserving Hardware](#privacy-preserving-hardware)



## General



General resources about PETs and related concepts



For general privacy tools and services, see the **Awesome Privacy List** (github: [pluja/awesome-privacy](https://github.com/pluja/awesome-privacy)).



### Articles



- [Privacy Enhancing Technologies: An Introduction for Technologists](https://martinfowler.com/articles/intro-pet.html) - Overview of PETs from Katharine Jarmul, the author of Practical Data Privacy.



### Books



- [Practical Data Privacy](https://www.oreilly.com/library/view/practical-data-privacy/9781098129453/) - An overview of privacy and how to apply it in technical systems and organizations. Includes introductions to various PETs.



- [Real World Cryptography](https://www.manning.com/books/real-world-cryptography) - The majority of the book is an introduction to cryptography and cryptographic applications with additional material covering PETs such as multi-party computation, homomorphic encryption, zero-knowledge proofs, and cryptographic hardware.



### Blogs



- [OpenMined Blog](https://blog.openmined.org/) - Blog of the open-source group OpenMined with several posts on PETs and privacy tech.



### Papers



- [Privacy enhancing technologies: Adoption guide](https://www.gov.uk/government/publications/privacy-enhancing-technologies-adoption-guide) - A white paper and interactive tool on adopting PETs for projects provided by the UK government's Centre for Data Ethics and Innovation.



- [The United Nations Guide on Privacy-Enhancing Technologies for Official Statistics](https://unstats.un.org/bigdata/task-teams/privacy/guide/2023_UN%20PET%20Guide.pdf)



### Podcasts



- [Shifting Privacy Left](https://shiftingprivacyleft.com/) - Podcast hosted by privacy and legal expert Debra J Farber featuring interviews and discussions with various privacy and PETs experts.



## Differential Privacy



See the **Awesome Differential Privacy List** (github: [menisadi/awesome-differential-privacy](https://github.com/menisadi/awesome-differential-privacy)).



### Organizations



- [DifferentialPrivacy.org](https://differentialprivacy.org/) - A website with resources curated by the DP research community. It includes several courses, videos, surveys, and links to other resources.



- [OpenDP](https://opendp.org/) - Open-source group developing DP tools.



### Blogs



- [NIST Differential Privacy Blog Series](https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/focus-areas/de-id/blog-series-1) - A blog series from NIST covering topics from introductory concepts to deploying DP-based applications.



- [A friendly, non-technical introduction to differential privacy](https://desfontain.es/privacy/friendly-intro-to-differential-privacy.html) - A useful series of posts and visualizations introducing DP and related concepts from Damien Desfontaines, a.k.a. Ted, a research scientist at [Tumult Labs](https://www.tmlt.io/).



### Books



- [The Algorithmic Foundations of Differential Privacy](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) - Free, online book focused on DP principles and theory



- [Programming Differential Privacy](https://programming-dp.com/) - Free, online book focused on teaching DP principles through Python code examples



- [Hands-On Differential Privacy](https://www.oreilly.com/library/view/hands-on-differential-privacy/9781492097730/) - An O'Reilly book analyzing practical DP methods with a focus on using the OpenDP library



### Open-Source Tools



- [OpenDP](https://github.com/opendp/opendp) - Python bindings for OpenDP's Rust-based framework.



### Papers



- [Guidelines for Evaluating Differential Privacy Guarantees](https://csrc.nist.gov/pubs/sp/800/226/ipd) - NIST publication providing an overview of DP with a focus on practical considerations and common pitfalls when using DP in practice.



- [Differential Privacy: A Primer for a Non-Technical Audience](https://scholarship.law.vanderbilt.edu/jetlaw/vol21/iss1/4/) - In-depth yet accessible overview of DP.



- [Issues Encountered Deploying Differential Privacy](https://arxiv.org/abs/1809.02201) - Review of deployment and usage of DP for the 2020 United States population census.



- [Differential Privacy & the 2020 U.S. Census Reading List](https://priyakalot.github.io/DP-census/) - Collection of writings and presentations reviewing the usage of DP in the 2020 U.S. Census.



### Videos



- [Protecting Privacy with MATH](https://www.youtube.com/watch?v=pT19VwBAqKA) - Overview of DP and related concepts by the [MinutePhysics](https://www.youtube.com/@MinutePhysics) channel in collaboration with the US Census Bureau.



## Privacy-Preserving Federated Learning



**Note**: Federated learning by itself does not guarantee data privacy, but when used in combination with other PETs it can become privacy-preserving.



There are several different Awesome lists for federated learning, but this one seems to be the most popular. Note that it's federated learning generally, not just PPFL: [https://github.com/innovation-cat/Awesome-Federated-Machine-Learning](https://github.com/innovation-cat/Awesome-Federated-Machine-Learning)



### Papers



- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://arxiv.org/abs/1602.05629) - Paper that introduced the popular FedAvg algorithm



### Open-Source Tools



- [APPFL](https://github.com/APPFL/APPFL) - Advanced Privacy-Preserving Federated Learning built my Argonne National Labs aimed at high-performance computing applications. Includes differential-privacy and the ability to incorporate other PETs



- [FedML](https://www.fedml.ai/) -  Github Repository: [https://github.com/fedml-ai/fedml](https://github.com/fedml-ai/fedml)



- [Flower](https://flower.dev/) - A unified FL framework available in multiple languages. Flower does not include privacy preservation itself, but is intended to be used in conjunction with other ML and PPML frameworks.



- [OpenFL](https://github.com/securefederatedai/openfl) - Open-source FL framework hosted by the Linux Foundation.



## Homomorphic Encryption



See the **Awesome Homomorphic Encryption List** (github: [jonaschn/awesome-he](https://github.com/jonaschn/awesome-he)).



### Organizations



- [https://homomorphicencryption.org/](Homomorphic Encryption Standardization) - A body consisting of industry, government, and academic members advocating for standardization of HE.



### Blogs



- [A High-Level Technical Overview of Fully Homomorphic Encryption](https://www.jeremykun.com/2024/05/04/fhe-overview/) - Great overview of FHE from Jeremy Kun's Math ∩ Programming blog



## Secure Multiparty Computation



See the **Awesome MPC List** (github: [rdragos/awesome-mpc](https://github.com/rdragos/awesome-mpc)).



### Organizations



- [MPC Alliance](https://www.mpcalliance.org/) - Industry group devoted to increasing adoption of MPC. Includes links to several books and videos.



### Videos



- [Multiparty Computation - Computational Thinking](https://www.youtube.com/watch?v=5qzNe1hk0oY) - 5 minute intro on MPC and secret sharing examples and concepts



## Synthetic Data



### Tools



[DataSynthesizer](https://github.com/DataResponsibly/DataSynthesizer) - A Python-based tool for generating differentally-private synthetic data



## Zero-knowledge Proofs and zk-SNARKs



See the **Awesome Zero Knowledge Proofs List** (github: [https://github.com/matter-labs/awesome-zero-knowledge-proofs](https://github.com/matter-labs/awesome-zero-knowledge-proofs))



### Organizations



- [ZKProof](https://zkproof.org/) - An open-industry academic initiative seeking to develop standards for ZKPs. The organization also conducts workshops, publishes research, educational material, etc.



### Blogs and Tutorials



- [Zero Knowledge What? An Introduction to Zero Knowledge](https://codethechange.stanford.edu/guides/guide_zk.html) - A technical introduction (including basic Python examples) from Stanford's [Code the Change](https://codethechange.stanford.edu/#/) guides.



- [Zero-Knowledge: a tutorial by Oded Goldreich](https://www.wisdom.weizmann.ac.il/~oded/zk-tut02.html) - A tutorial covering introductory and advanced topics in Zero-knowledge proofs



### Papers



- [The knowledge complexity of interactive proof-systems](https://dl.acm.org/doi/10.1145/22145.22178) - The original paper introducing zero-knowledge proofs



- [A physical zero-knowledge object-comparison system for nuclear warhead verification](https://arxiv.org/abs/1602.07717) - A proposed mechanism to use ZKPs for nuclear armament reduction



- [Why and How zk-SNARK Works](https://arxiv.org/abs/1906.07221) - Good overview of the building blocks that zk-SNARKs are composed of



### Videos



- [Zero Knowledge Proofs - Computational Thinking](https://www.youtube.com/watch?v=5qzNe1hk0oY) - 9 minute intro on ZKP examples and concepts



## Privacy-preserving Hardware



### Blogs



- [State of the Future - Trusted Execution Environments (TEEs) and Confidential Computing](https://stateofthefuture.substack.com/p/e19-trusted-execution-environments) - Overview from a VC blog on what trusted-execution environments are and how they enable privacy-preserving, collaborative computing



## Contribute



Contributions welcome! Read the [contribution guidelines](contributing.md) first.