Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.

A curated list of trustworthy deep learning papers. Daily updating...

[NeurIPS 2024] "Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration"

Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)

[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

Code for ML Doctor

[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

RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models. NeurIPS 2024

Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.

Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)

🔒 Implementation of Shokri et al(2016) "Membership Inference Attacks against Machine Learning Models"

reveal the vulnerabilities of SplitNN

Min-K%++: Improved baseline for detecting pre-training data of LLMs https://arxiv.org/abs/2404.02936

Collection of tools and resources for managing the statistical disclosure control of trained machine learning models

Differential Privacy Protection against MembershipInference Attack on Machine Learning for Genomic Data

The official implementation of the paper "Data Contamination Calibration for Black-box LLMs" (ACL 2024)

FederBoost's Federated Gradient Boosting Decision Tree Algorithm, Federated enabled Membership Inference

Bachelor's Thesis on Membership Inference Attacks

Codebase for Active Membership Inference Attack under Local Differential Privacy in Federated Learning