Awesome Machine Unlearning (A Survey of Machine Unlearning)

[NeurIPS D&B '25] The one-stop repository for LLM unlearning

Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)

Privacy Testing for Deep Learning

Python package for measuring memorization in LLMs.

[ICLR24 (Spotlight)] "SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation" by Chongyu Fan*, Jiancheng Liu*, Yihua Zhang, Eric Wong, Dennis Wei, Sijia Liu

[NeurIPS23 (Spotlight)] "Model Sparsity Can Simplify Machine Unlearning" by Jinghan Jia*, Jiancheng Liu*, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu

What does gpt-oss tell us about OpenAI's training data?

reveal the vulnerabilities of SplitNN

Exploring how GANs leak private training data through MIA, and whether DP can stop it

[ICMLW 2026] The official repository for our paper, "Machine Text Detectors are Membership Inference Attacks"

A repository about literature of copyright protection in deep learning.

(ACL 2026 Main) LLMSurgeon recovers the pretraining data mixture of any LLM from only its generated text — no weights, no training data. A calibrated domain classifier plus label-shift correction de-blurs biased predictions. Ships with LLMScan, a benchmark on 8 open-source LLMs.

Membership inference attack simulator: overfitting leaks training data, differential privacy defends against it

Reproducible research scaffolding for privacy-risk auditing of diffusion models.

A continuously-updated catalog of machine unlearning papers with live citation counts.

An LLM Privacy Risk Evaluation Tool that probes AI models for PII generation risk, training data regurgitation, and membership inference signals.

SecureMed-LLM: A privacy-preserving framework for clinical report generation from chest X-rays, integrating Med-Guard anonymization, DP-SGD (ε=3.0), adversarial training, IDS-LLM validation, and ECIES/Curve25519 encryption. PeerJ Computer Science 2025.

🤖 AI Insights Suite — NLP tools for privacy, explainability and membership inference attacks

Unveiling Privacy Risks in the Long Tail: Membership Inference in Class Skewness