ml_privacy_meter
Privacy Auditor
An auditing tool to assess the privacy risks of machine learning models
Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.
604 stars
18 watching
100 forks
Language: Python
last commit: 6 days ago data-privacydata-protectiondata-protection-impact-assessmentexplainable-aigdprinferenceinformation-leakagemachine-learningmembership-inference-attackprivacyprivacy-audit
Related projects:
Repository | Description | Stars |
---|---|---|
tensorflow/privacy | A Python library for training machine learning models while preserving the privacy of sensitive data | 1,943 |
privacytrustlab/bias_in_fl | Analyzing bias propagation in federated learning algorithms to improve group fairness and robustness | 11 |
iamgroot42/mimir | Measures memorization in Large Language Models (LLMs) to detect potential privacy issues | 121 |
h1r0gh057/anonymous | A Python implementation of a tool designed to provide anonymity and privacy in software development and data analysis. | 1,838 |
microsoft/private-benchmarking | A platform for private benchmarking of machine learning models with different trust levels. | 6 |
freedomintelligence/mllm-bench | Evaluates and compares the performance of multimodal large language models on various tasks | 55 |
abhinav-bohra/privacy-preserving-ml | Implementing an SVM model to make predictions on encrypted data while preserving the client's privacy | 1 |
algofairness/blackboxauditing | A software package for auditing and analyzing machine learning models to detect unfair biases | 130 |
eric-ai-lab/fedvln | An open-source implementation of a federated learning framework to protect data privacy in embodied agent learning for Vision-and-Language Navigation. | 13 |
monalabs/mona-openai | An integration client providing real-time monitoring and analysis of OpenAI API usage | 93 |
openmined/private-ai-resources | A curated collection of resources and libraries for secure machine learning research and development | 471 |
adebayoj/fairml | An auditing toolbox to assess the fairness of black-box predictive models | 360 |
shreya-28/secure-ml | Secure Linear Regression in the Semi-Honest Two-Party Setting. | 38 |
jphall663/interpretable_machine_learning_with_python | Teaching software developers how to build transparent and explainable machine learning models using Python | 673 |
nyu-mll/bbq | A dataset and benchmarking framework to evaluate the performance of question answering models on detecting and mitigating social biases. | 87 |