- SecureBoost: A Lossless Federated Learning Framework [Paper]
- Fair Resource Allocation in Federated Learning [Paper]
- How To Backdoor Federated Learning [Paper] [[Github]](https://github.com/ebagdasa/
- Inverting Gradients - How easy is it to break privacy in federated learning? [Paper] [NIPS2020]
- Fair Resource Allocation in Federated Learning [Paper]
- How To Backdoor Federated Learning [Paper] [Github]
- Can You Really Backdoor Federated Learning? [Paper]
- Model Poisoning Attacks in Federated Learning [Paper] [NIPS workshop 2018]
- Inverting Gradients - How easy is it to break privacy in federated learning? [Paper] [NIPS2020]
- Attack of the Tails: Yes, You Really Can Backdoor Federated Learning [Paper] [NIPS2020]
- Membership Inference Attacks Against Machine Learning Models [Paper] [Github] [Cornell]
- Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks [Paper] [NIPS2020]
- Distributed Newton Can Communicate Less and Resist Byzantine Workers [Paper] [Berkeley] [NIPS2020]
- Byzantine Resilient Distributed Multi-Task Learning [Paper] [NIPS2020]
- A Scalable Approach for Privacy-Preserving Collaborative Machine Learning [Paper] [USC] [NIPS2020]
- Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning [Paper] [NIPS 2019 Workshop]
- Quantification of the Leakage in Federated Learning [Paper]
- Federated learning: distributed machine learning with data locality and privacy [Blog]
- A Hybrid Approach to Privacy-Preserving Federated Learning [Paper]
- Analyzing Federated Learning through an Adversarial Lens [Paper]
- Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attack [Paper]
- Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning [Paper]
- Analyzing Federated Learning through an Adversarial Lens [Paper]
- Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning [Paper]
- Protection Against Reconstruction and Its Applications in Private Federated Learning [Paper]
- Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing [Paper]
- Privacy-Preserving Collaborative Deep Learning with Unreliable Participants [Paper]
- Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning [Paper] [ICLR 2021]
- Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting [Paper]
- Privacy-Preserving Deep Learning via Weight Transmission [Paper]
- Learning Private Neural Language Modeling with Attentive Aggregation [Paper], IJCNN 2019 [Code]
- Exploiting Unintended Feature Leakage in Collaborative Learning, an attack method related to membership inference [Paper] [Github]
- Applied Cryptography [Udacity]
- Cryptography basics
- A Brief Introduction to Differential Privacy [Blog]
- Deep Learning with Differential Privacy [Paper]
- Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang.
- Differentially-Private Federated Linear Bandits [Paper] [Slides] [MIT] [NIPS2020]
- Learning Differentially Private Recurrent Language Models [Paper]
- Federated Learning with Bayesian Differential Privacy [Paper] [NIPS 2019 Workshop]
- Private Federated Learning with Domain Adaptation [Paper] [NIPS 2019 Workshop]
- cpSGD: Communication-efficient and differentially-private distributed SGD [Paper]
- Federated Learning with Bayesian Differential Privacy [Paper] [NIPS 2019 Workshop]
- Differentially Private Data Generative Models [Paper]
- Differentially Private Federated Learning: A Client Level Perspective [Paper] [Github] [ NIPS2017 Workshop]
- Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data [Paper]
- Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, and Kunal Talwar.
- Private Aggregation of Teacher Ensembles (PATE)
- Scalable Private Learning with PATE [Paper]
- Extension of PATE
- The original PATE paper at ICLR 2017 and recording of the ICLR oral
- The ICLR 2018 paper on scaling PATE to large number of classes and imbalanced data.
- GitHub code repo for PATE
- GitHub code repo for the refined privacy analysis of PATE
- Simple Introduction to Sharmir's Secret Sharing and Lagrange Interpolation [Youtube]
- Secret Sharing, Part 1 [Blog]: Shamir's Secret Sharing & Packed Variant
- Secret Sharing, Part 2 [Blog]: Improve efficiency
- Secret Sharing, Part 3 [Blog]
-
Basics of Secure Multiparty Computation [Youtube]: based on Shamir's Secret Sharing
-
What is SPDZ?
-
The SPDZ Protocol [Blog]: implementation codes included
- Multiparty Computation from Somewhat Homomorphic Encryption [Paper]
- SPDZ introduction
- Practical Covertly Secure MPC for Dishonest Majority – or: Breaking the SPDZ Limits [Paper]
- MASCOT: Faster Malicious Arithmetic Secure Computation with Oblivious Transfer [Paper]
- Removing the crypto provider and instead letting the parties generate these triples on their own
- Overdrive: Making SPDZ Great Again [Paper]
- Safetynets: Verifiable execution of deep neural networks on an untrusted cloud
-
Building Safe A.I. [Blog]
- A Tutorial for Encrypted Deep Learning
- Use Homomorphic Encryption (HE)
-
Private Deep Learning with MPC [Blog]
- A Simple Tutorial from Scratch
- Use Multiparty Compuation (MPC)
-
Private Image Analysis with MPC [Blog]
- Training CNNs on Sensitive Data
- Use SPDZ as MPC protocol
Helen: Maliciously Secure Coopetitive Learning for Linear Models [Paper] [NIPS 2019 Workshop]
- Privacy-Preserving Deep Learning [Paper]
- Privacy Partition: A Privacy-Preserving Framework for Deep Neural Networks in Edge Networks [Paper]
- Practical Secure Aggregation for Privacy-Preserving Machine Learning [Paper] (Google)
- Secure Aggregation: The problem of computing a multiparty sum where no party reveals its update in the clear—even to the aggregator
- Goal: securely computing sums of vectors, which has a constant number of rounds, low communication overhead, robustness to failures, and which requires only one server with limited trust
- Need to have basic knowledge of cryptographic algorithms such as secret sharing, key agreement, etc.
- Practical Secure Aggregation for Federated Learning on User-Held Data [Paper] (Google)
- Highly related to Practical Secure Aggregation for Privacy-Preserving Machine Learning
- Proposed 4 protocol one by one with gradual improvement to meet the requirement of secure aggregation propocol.
- SecureML: A System for Scalable Privacy-Preserving Machine Learning [Paper]
- DeepSecure: Scalable Provably-Secure Deep Learning [Paper]
- Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications [Paper]
- Contains several MPC frameworks