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practical-python

Practical Python Programming (course by @dabeaz)

premo

EDA of COVID-19 dataset and building ML models

primia

PriMIA: Privacy-preserving Medical Image Analysis

privacy-engineering

private-ml-for-health

Dopamine: Differentially Private Federated Learning on Medical Data (AAAI - PPAI)

pysyft

Data science on data without acquiring a copy

resistancepoisoningfederatedmalwareclassifier

Mobile devices contain highly sensitive data, making them an attractive target to attackers. As an Android malware classifier, LiM aims to tackle security issues while respecting the privacy of users by leveraging the power of federated learning. Compared to centralized ways of learning, the unique properties of federated learning open up new attack surfaces for adversaries. For instance, an adversary can attempt to let a targeted malicious app be misclassified as clean by sending poisoned model updates in the federation. This work builds on LiM with the aim of improving its resistance against these poisoning attacks. First, I formulate and test several targeted model update poisoning attacks. Depending on assumptions regarding the adversary's knowledge, the attacks are able to successfully compromise around 10 to 25\% of the honest client devices in the federation. Second, while most defenses result in a trade-off between improving resistance and maintaining performance, I propose a simple defense strategy that can never decrease the performance of the federation. Against a strong adversary, who has knowledge of the algorithm used to aggregate the model updates, the defense was mostly insufficient to prevent poisoning. In the presence of a more realistic adversary, the defense caused LiM to regain best-case performance, comparable to the performance in a scenario without adversary.

rq-vae-transformer

The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22)

streamlit-example

Example Streamlit app that you can fork to test out share.streamlit.io