Discover cleverhans-lab/capc-iclr Open Source project

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Language
Python
License
MIT License
Created over 3 years ago
Updated over 2 years ago

cleverhans-lab

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CaPC is a method that enables collaborating parties to improve their own local heterogeneous machine learning models in a setting where both confidentiality and privacy need to be preserved to prevent explicit and implicit sharing of private data.

cleverhans

An adversarial example library for constructing attacks, building defenses, and benchmarking both

machine-unlearning

entangled-watermark

Proof-of-Learning

dataset-inference

[ICLR'21] Dataset Inference for Ownership Resolution in Machine Learning

Jupyter Notebook

unrolling-sgd

code release for "Unrolling SGD: Understanding Factors Influencing Machine Unlearning" published at EuroS&P'22

Python

verifiable-unlearning

Python

model-extraction-iclr

DeCaPH

deepfake_attribution

Zest-Model-Distance

DatasetInferenceForSelfSupervisedModels

Python

capc-demo

Jupyter Notebook

FRAUD-Detect

Official implementation of Washing The Unwashable : On The (Im)possibility of Fairwashing Detection, NeurIPS 2022

Python

ssl-attacks-defenses

On the Difficulty of Defending Self-Supervised Learning against Model Extraction

Python

Gradients-Look-Alike-Sensitivity-is-Often-Overestimated-in-DP-SGD

Python

Forging

Jupyter Notebook

monte-carlo-adv

Python

private-multi-winner-voting

New privacy-preserving mechanisms for multi-label classification.

Python

FairFeedbackLoops

Python

PrivatePrompts

Code for the differential learning algorithms for soft and discrete prompts.

Python

cleverhans-lab/capc-iclr

cleverhans-lab

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