{"id":33176687,"url":"https://github.com/OpenMined/private-ai-resources","last_synced_at":"2026-01-17T06:00:37.171Z","repository":{"id":38419330,"uuid":"124164643","full_name":"OpenMined/private-ai-resources","owner":"OpenMined","description":"SOON TO BE DEPRECATED - Private machine learning 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[openmined-website](https://github.com/OpenMined/openmined-website).\n\n# Private-Ai-Resources\nPrivate machine learning progress\n\n## Content\n- [About](#about)\n- [Secure and Private AI Course from Udacity](#secure-and-private-ai-course)\n- [Secure Deep Learning](#secure-deep-learning)\n- [Libraries and Frameworks](#libraries-and-frameworks)\n- [General Research](#general-research)\n- [Blogs](#blogs)\n- [Groups](#groups)\n- [Thanks](#thanks)\n\n# About\n\nThis is a curated list of resources related to the research and development of private machine learning.\n\n# Secure and Private AI Course\n\n* [Secure and Private AI Course from Udacity](https://www.udacity.com/course/secure-and-private-ai--ud185)\n* [Notebooks for Secure and Private AI Course from Udacity](https://github.com/udacity/private-ai)\n* [Advanced PySyft](https://github.com/OpenMined/PySyft/tree/master/examples/tutorials)\n* [Advanced PyGrid](https://github.com/OpenMined/PyGrid/tree/dev/examples)\n\n\n# Secure Deep Learning\n\n* [PySyft: A Generic Framework for Privacy Preserving Deep Learning](https://arxiv.org/abs/1811.04017)\n* [Private Deep Learning in TensorFlow Using Secure Computation, October 23, 2018](https://arxiv.org/abs/1810.08130)\n* [SecureNN: Efficient and Private Neural Network Training, May 10,2018](https://eprint.iacr.org/2018/442.pdf)\n* [Gazelle: A Low Latency Framework for Secure Neural Network Inference, January 16, 2018](https://arxiv.org/abs/1801.05507)\n* [Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications, November 29, 2017](https://eprint.iacr.org/2017/1164)\n* [CryptoDL: Deep Neural Networks over Encrypted Data, November 14, 2017](https://arxiv.org/abs/1711.05189)\n* [MiniONN: Oblivious Neural Network Predictions via MiniONN\nTransformations, November 3, 2017](https://acmccs.github.io/papers/p619-liuA.pdf)\n* [DeepSecure: Scalable Provably-Secure Deep Learning, May 24, 2017](https://arxiv.org/abs/1705.08963)\n* [SecureML: A System for Scalable Privacy-Preserving Machine Learning, April 19, 2017](https://eprint.iacr.org/2017/396)\n* [CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy, February 24, 2016](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/04/CryptonetsTechReport.pdf)\n* [Privacy-Preserving Deep Learning, October 12, 2015](https://dl.acm.org/citation.cfm?id=2813687)\n\n\n# Libraries and Frameworks\n\n* [TinyGarble: Logic Synthesis and Sequential Descriptions for Yao's Garbled Circuits](https://github.com/esonghori/TinyGarble)\n* [SPDZ-2: Multiparty computation with SPDZ and MASCOT offline phase](https://github.com/bristolcrypto/SPDZ-2)\n* [ABY: A Framework for Efficient Mixed-Protocol Secure Two-Party Computation](https://github.com/encryptogroup/aby)\n* [Obliv - C: C compiler for embedding privacy preserving protocols:](http://oblivc.org/)\n* [TFHE: Fast Fully Homomorphic Encryption Library over the Torus](https://github.com/tfhe/tfhe)\n* [SEAL: Simple Encypted Arithmatic Library](https://www.microsoft.com/en-us/research/project/simple-encrypted-arithmetic-library/)\n* [PySEAL: Python interface to SEAL](https://github.com/Lab41/PySEAL)\n* [HElib: An Implementation of homomorphic encryption](https://github.com/shaih/HElib)\n* [nGraph-HE: Deep learning with Homomorphic Encryption (HE) through Intel nGraph](https://github.com/NervanaSystems/he-transformer)\n\n\n# General Research\n\n* [Overdrive: Making SPDZ Great Again](https://eprint.iacr.org/2017/1230)\n* [Privacy-Preserving Logistic Regression Training](https://eprint.iacr.org/2018/233)\n* [Between a Rock and a Hard Place: Interpolating Between MPC and FHE](https://eprint.iacr.org/2013/085.pdf)\n* [Privacy-Preserving Boosting with Random Linear Classifiers for Learning from User-Generated Data](https://arxiv.org/abs/1802.08288)\n* [The Secret Sharer: Measuring Unintended Neural Network Memorization \u0026 Extracting Secrets](https://arxiv.org/abs/1802.08232)\n* [Improvements for Gate-Hiding Garbled Circuits](https://eprint.iacr.org/2017/976.pdf)\n* [Practical Secure Aggregation for Privacy-Preserving Machine Learning](https://eprint.iacr.org/2017/281.pdf)\n* [CryptoRec: Secure Recommendations as a Service](https://arxiv.org/pdf/1802.02432.pdf)\n* [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://arxiv.org/abs/1610.05755)\n* [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://arxiv.org/abs/1602.05629)\n* [Differentially Private Generative Adversarial Network](https://arxiv.org/abs/1802.06739)\n* [Doing Real Work with FHE: The Case of Logistic Regression](https://eprint.iacr.org/2018/202)\n* [ADSNARK: Nearly Practical and Privacy-Preserving Proofs on Authenticated Data](https://eprint.iacr.org/2014/617.pdf)\n* [Scalable Private Learning with PATE](https://arxiv.org/abs/1802.08908)\n* [Doing Real Work with FHE: The Case of Logistic Regression](https://eprint.iacr.org/2018/202)\n* [Reading in the Dark: Classifying Encrypted Digits with Functional Encryption](https://eprint.iacr.org/2018/206)\n* [Stealing Hyperparameters in Machine Learning](https://arxiv.org/abs/1802.05351)\n* [How to Backdoor Federated Learning](https://arxiv.org/abs/1807.00459)\n* [Federated Optimization:Distributed Machine Learning for On-Device Intelligence](https://arxiv.org/abs/1610.02527)\n* [Federated Learning: Strategies for Improving Communicating Efficiency](https://arxiv.org/abs/1610.05492)\n* [Personalized and Private Peer-to-Peer Machine Learning](http://proceedings.mlr.press/v84/bellet18a/bellet18a.pdf)\n* [A generic framework forprivacy preserving deep learning](https://arxiv.org/abs/1811.04017) \n* [Protection Against Reconstruction and Its Applications in Private Federated Learning](https://arxiv.org/abs/1812.00984)\n* [Towards Federated Learning at Scale: System Design](https://arxiv.org/abs/1902.01046)\n* [Federated Learning of Deep Networks using Model Averaging](https://arxiv.org/abs/1602.05629)\n* [SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search](https://arxiv.org/abs/1904.02033)\n\n\n# Blogs\n\n* [Cryptography and Machine Learning: Mixing both for private data analysis](https://mortendahl.github.io/)\n* [Building Safe A.I.: A Tutorial for Encrypted Deep Learning](https://iamtrask.github.io/2017/03/17/safe-ai/)\n* [Awesome MPC: Curated List of resources for MPC](https://github.com/rdragos/awesome-mpc)\n\n# Groups\n\n* [The Alan Turing Institute: Privacy-preserving data analysis](https://www.turing.ac.uk/research_projects/privacy-preserving-data-analysis/)\n\n# Podcasts\n\n* [TWiML: Differential Privacy Theory \u0026 Practice. Aaron Roth](https://twimlai.com/talk/132)\n* [TWiML: Scalable Differential Privacy for Deep Learning. Nicholas Papernot](https://twimlai.com/talk/134)\n\n# Workshops\n\n* [Privacy Preserving Machine Learning NeurIPS 2018 Workshop](https://ppml-workshop.github.io/ppml/)\n\n# Thanks\n\n### Maintainers\n\n* [@gavinuhma](https://github.com/gavinuhma)\n* [@iamtrask](https://github.com/iamtrask)\n* [@robert-wagner](https://github.com/robert-wagner)\n* [@mortendahl](https://github.com/mortendahl)\n\n### OpenMined Community\n\nThanks to members of the OpenMined community who have shared links on slack: [@morgangiraud](https://github.com/morgangiraud), [@jvmancuso](https://github.com/jvmancuso)\n\n### Adding links\n\nIf 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!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FOpenMined%2Fprivate-ai-resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FOpenMined%2Fprivate-ai-resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FOpenMined%2Fprivate-ai-resources/lists"}