Library for training machine learning models with privacy for training data
tensorflow/privacy की मुख्य विशेषताएं हैं: AI Security and Red Teaming, Model Fairness And Privacy, Privacy and Safety।
tensorflow/privacy के ओपन-सोर्स विकल्पों में शामिल हैं: nvidia/nemo-guardrails — NeMo-Guardrails is a toolkit for adding programmable safety constraints and dialogue boundaries to large language… trusted-ai/adversarial-robustness-toolbox — The Adversarial Robustness Toolbox (ART) is an open-source library that provides a unified framework for evaluating,… trigaten/learn_prompting — Learn_Prompting is an educational project focused on prompt engineering, providing the principles and techniques… azure/counterfit. cleverhans-lab/cleverhans — Cleverhans is an adversarial machine learning library and toolkit designed to generate adversarial examples,… bethgelab/foolbox — .. raw:: html.
NeMo-Guardrails is a toolkit for adding programmable safety constraints and dialogue boundaries to large language model conversational systems. It functions as security middleware that intercepts inputs and outputs to block prompt injections, jailbreaks, and sensitive data leaks, while providing a conversational dialogue manager to define structured interaction flows through configuration files. The framework includes a hallucination filter to screen model outputs for factual accuracy and a specialized modeling language for defining conversational flows and constraints. It provides capabiliti
The Adversarial Robustness Toolbox (ART) is an open-source library that provides a unified framework for evaluating, defending, and certifying machine learning models against adversarial threats. It wraps models from any framework behind a common estimator interface, enabling composable pipelines for attack generation, defense application, robustness certification, and privacy auditing across evasion, poisoning, and extraction threats. The library distinguishes itself by covering the full adversarial ML security lifecycle within a single toolkit. It supports gradient-based adversarial example
Learn_Prompting is an educational project focused on prompt engineering, providing the principles and techniques required to craft effective inputs and improve the quality of generative AI outputs. The project covers advanced prompting strategies to enhance reasoning, reliability, and output quality. This includes techniques for task decomposition, chain-of-thought reasoning, and the use of few-shot and zero-shot guidance. It also addresses model security through the study of prompt hacking, vulnerability analysis, and privacy auditing to prevent sensitive data leaks. The scope extends to th