Unlearn-Saliency

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

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

? Model-Driven test data generation platform enabling developers to create realistic, scalable, and privacy-compliant test data. Features model-driven data generation, GDPR compliance, and seamless Python integration.

"Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning" by Chongyu Fan*, Jiancheng Liu*, Licong Lin*, Jinghan Jia, Ruiqi Zhang, Song Mei, Sijia Liu

Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs (ACM CCS'21)

Location Privacy Meter: A tool to model human mobility and quantify location privacy

My public and contributable base/wiki/digital garden for cybersecurity, cyber resilience, and cyber-related topics - mixed with some personal. Utilizes Notion, Obsidian, and Github to sync content.

Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning

Discover the art of enhancing your data using generative modelling in these notebooks.

The implementation of our paper 'Visual Privacy Protection via Mapping Distortion', accepted by the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021.