OpenMined / Private Ai Resources
Licence: mit
SOON TO BE DEPRECATED - Private machine learning progress
Stars: ✭ 461
Labels
Projects that are alternatives of or similar to Private Ai Resources
Yarneditor
A tool for writing interactive dialogue in games!
Stars: ✭ 292 (-36.66%)
Mutual labels: writing
idyll-studio
A graphical editor for creating Idyll documents.
Stars: ✭ 63 (-86.33%)
Mutual labels: writing
msk-markdown
MSK Markdown is light markdown editor and viewer.
Stars: ✭ 99 (-78.52%)
Mutual labels: writing
eureka
✍️ I read, I write, I think, I do, I learn, I code.
Stars: ✭ 106 (-77.01%)
Mutual labels: writing
Fountain Mode
Emacs major mode for screenwriting in Fountain plain-text markup
Stars: ✭ 288 (-37.53%)
Mutual labels: writing
markdown-memo
Compile simple (or not so simple) Markdown memos to html and/or pdf via LaTeX with pandoc.
Stars: ✭ 19 (-95.88%)
Mutual labels: writing
140stories
140Stories: Collaborative stories 140 chars at a time.
Stars: ✭ 14 (-96.96%)
Mutual labels: writing
stylo
Stylo est un éditeur de textes pour articles scientifiques en sciences humaines et sociales.
Stars: ✭ 29 (-93.71%)
Mutual labels: writing
To Title Case
A JavaScript method for intelligently converting strings to title case.
Stars: ✭ 306 (-33.62%)
Mutual labels: writing
Pandoc Starter
📄 My pandoc markdown templates and makefiles
Stars: ✭ 443 (-3.9%)
Mutual labels: writing
Gojot
A command-line journal that is distributed and encrypted, making it easy to jot notes 📓
Stars: ✭ 340 (-26.25%)
Mutual labels: writing
Org Transclusion
(alpha) Emacs package to enable transclusion with Org Mode
Stars: ✭ 251 (-45.55%)
Mutual labels: writing
DEPRECATION NOTICE
Warning, this repository will soon be deprecated in favor of openmined-website.
Private-Ai-Resources
Private machine learning progress
Content
- About
- Secure and Private AI Course from Udacity
- Secure Deep Learning
- Libraries and Frameworks
- General Research
- Blogs
- Groups
- Thanks
About
This is a curated list of resources related to the research and development of private machine learning.
Secure and Private AI Course
- Secure and Private AI Course from Udacity
- Notebooks for Secure and Private AI Course from Udacity
- Advanced PySyft
- Advanced PyGrid
Secure Deep Learning
- PySyft: A Generic Framework for Privacy Preserving Deep Learning
- Private Deep Learning in TensorFlow Using Secure Computation, October 23, 2018
- SecureNN: Efficient and Private Neural Network Training, May 10,2018
- Gazelle: A Low Latency Framework for Secure Neural Network Inference, January 16, 2018
- Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications, November 29, 2017
- CryptoDL: Deep Neural Networks over Encrypted Data, November 14, 2017
- MiniONN: Oblivious Neural Network Predictions via MiniONN Transformations, November 3, 2017
- DeepSecure: Scalable Provably-Secure Deep Learning, May 24, 2017
- SecureML: A System for Scalable Privacy-Preserving Machine Learning, April 19, 2017
- CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy, February 24, 2016
- Privacy-Preserving Deep Learning, October 12, 2015
Libraries and Frameworks
- TinyGarble: Logic Synthesis and Sequential Descriptions for Yao's Garbled Circuits
- SPDZ-2: Multiparty computation with SPDZ and MASCOT offline phase
- ABY: A Framework for Efficient Mixed-Protocol Secure Two-Party Computation
- Obliv - C: C compiler for embedding privacy preserving protocols:
- TFHE: Fast Fully Homomorphic Encryption Library over the Torus
- SEAL: Simple Encypted Arithmatic Library
- PySEAL: Python interface to SEAL
- HElib: An Implementation of homomorphic encryption
- nGraph-HE: Deep learning with Homomorphic Encryption (HE) through Intel nGraph
General Research
- Overdrive: Making SPDZ Great Again
- Privacy-Preserving Logistic Regression Training
- Between a Rock and a Hard Place: Interpolating Between MPC and FHE
- Privacy-Preserving Boosting with Random Linear Classifiers for Learning from User-Generated Data
- The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets
- Improvements for Gate-Hiding Garbled Circuits
- Practical Secure Aggregation for Privacy-Preserving Machine Learning
- CryptoRec: Secure Recommendations as a Service
- Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- Differentially Private Generative Adversarial Network
- Doing Real Work with FHE: The Case of Logistic Regression
- ADSNARK: Nearly Practical and Privacy-Preserving Proofs on Authenticated Data
- Scalable Private Learning with PATE
- Doing Real Work with FHE: The Case of Logistic Regression
- Reading in the Dark: Classifying Encrypted Digits with Functional Encryption
- Stealing Hyperparameters in Machine Learning
- How to Backdoor Federated Learning
- Federated Optimization:Distributed Machine Learning for On-Device Intelligence
- Federated Learning: Strategies for Improving Communicating Efficiency
- Personalized and Private Peer-to-Peer Machine Learning
- A generic framework forprivacy preserving deep learning
- Protection Against Reconstruction and Its Applications in Private Federated Learning
- Towards Federated Learning at Scale: System Design
- Federated Learning of Deep Networks using Model Averaging
- SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search
Blogs
- Cryptography and Machine Learning: Mixing both for private data analysis
- Building Safe A.I.: A Tutorial for Encrypted Deep Learning
- Awesome MPC: Curated List of resources for MPC
Groups
Podcasts
- TWiML: Differential Privacy Theory & Practice. Aaron Roth
- TWiML: Scalable Differential Privacy for Deep Learning. Nicholas Papernot
Workshops
Thanks
Maintainers
OpenMined Community
Thanks to members of the OpenMined community who have shared links on slack: @morgangiraud, @jvmancuso
Adding links
If you have any links to add please send a pull request, and we'll take a look. There is so much happening in this space!
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].