Collection of ε-differentially private algorithms and privacy attacks
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Updated
May 12, 2020 - Python
Collection of ε-differentially private algorithms and privacy attacks
Java K Anonymity and Differential privacy (Gaussian/Laplacian) Server, April 2021
Produces a differentially-private model for domain generation algorithm detection.
Securing Collaborative Medical AI by Using Differential Privacy
Works of SPAIC by Udacity and Facebook
A golang module for differential privacy
R ports of examples from Google's Differential Privacy repository.
CS6780 “Advanced Machine Learning”. Implemented multiple Federated Learning averaging methods in a Differentially Private setting and measured relative impact on model accuracy and fairness. Worked jointly with Caleb Berman of the Cornell MPS program
An example of differential privacy with laplace mechanism in federated learning using Pytorch
Official implementation for paper "FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition"
Bias evaluation of Differentially Private NLP models
A Python Package for NLP obfuscation using Differential Privacy
Common Data Model Project by SNUBH-SNU
Differential privacy for sklearn pipelines.
Testing membership inference attacks on Deep learning models (LSTM, CNN);
Li X, Chen Y, Wang C, Shen C. When Deep Learning Meets Differential Privacy: Privacy, Security, and More. IEEE Network. 2021 Nov;35(6):148-55.
Mentor: Ruobin(Robin) Gong; Collaborators: Leah Ghazali, Nami Jain, Yuexin Zhang
Differential private deep learning training performs well with Memorization Informed Frèchet Distance.
[ICLR 2023] Official JAX/Haiku implementation of the paper "Differentially Private Adaptive Optimization with Delayed Preconditioners"
Python package designed to facilitate the end-to-end production of differentially private synthetic data
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