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TensorFlow Privacy

Appears in 4 awesome lists

A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.

Open github.comtensorflow/privacy

Found in these lists

Awesome MLOps

Section: Model Fairness and Privacy · Library for training machine learning models with privacy for training data.

FreshScore 80

SOCIAL MEDIA

Section: LLM Security & AI Security · Library for training ML models with differential privacy.

FreshScore 86

Awesome Production Machine Learning

Section: Privacy and Safety · A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.

FreshScore 92

Table of Contents

Section: Differential Privacy Learning Resources

SlowScore 61

Netron

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Guardrails AI

Input/output validation framework for building reliable AI applications. Detects and mitigates risks through composable validators for PII, toxicity, prompt injection, and structured output validation. Features Guardrails Hub with 50+ pre-built validators. Apache 2.0 licensed.

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Portkey AI Gateway

🟢🟠 — Open AI gateway with provider routing, fallback and retry controls, guardrail integrations, observability, and MCP traffic support for model and agent applications. (Portkey) — note: the gateway is general infrastructure rather than a standalone security scanner; model-provider…

In 6 listsDetails

TextAttack

Python framework for adversarial attacks, data augmentation, and model training in NLP. Augment datasets to increase model robustness and generate adversarial examples. MIT licensed.

In 6 listsDetails

PySyft

is a Python library for secure and private Deep Learning. PySyft decouples private data from model training, using Federated Learning, Differential Privacy, and Encrypted Computation (like Multi-Party Computation (MPC) and Homomorphic Encryption (HE) within the main Deep Learning frameworks like…

In 5 listsDetails

ART

🟢 — Flagship machine-learning security library for evaluating and defending models against evasion, poisoning, extraction, and inference attacks across major ML frameworks. (LF AI & Data / IBM) · updated 2025-11-13); Related: Foolbox · PrivacyRaven

In 4 listsDetails