cleverhans-lab/PrivatePrompts

Stars
1
Language
Python
License
MIT License
Created about 1 year ago
Updated about 1 year ago

cleverhans-lab/PrivatePrompts

cleverhans-lab

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

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

cleverhans

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

Jupyter Notebook

machine-unlearning

entangled-watermark

Proof-of-Learning

dataset-inference

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

Jupyter Notebook

capc-iclr

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.

unrolling-sgd

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

verifiable-unlearning

model-extraction-iclr

DeCaPH

deepfake_attribution

Zest-Model-Distance

DatasetInferenceForSelfSupervisedModels

capc-demo

Jupyter Notebook

FRAUD-Detect

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

ssl-attacks-defenses

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

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

Forging

Jupyter Notebook

monte-carlo-adv

private-multi-winner-voting

New privacy-preserving mechanisms for multi-label classification.

FairFeedbackLoops