Privacy
Python package for simple implementations of state-of-the-art LDP frequency estimation algorithms. Contains code for our VLDB 2021 Paper.
Code for Canonne-Kamath-Steinke paper https://arxiv.org/abs/2004.00010
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Private Adaptive Optimization with Side Information (ICML '22)
Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.
A codebase that makes differentially private training of transformers easy.
[ICLR'24 Spotlight] DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer
Fast suffix arrays for Rust (with Unicode support).
🤫 Code and benchmark for our ICLR 2024 spotlight paper: "Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory"