---
type: Glossary Term
title: Differential Privacy for ML
description: Differential Privacy for ML is a mathematical framework that provides formal guarantees about the privacy of individuals whose data is used in machine learning.
resource: "https://www.contextstudios.ai/glossary/differential-privacy-ml"
category: security
language: en
timestamp: "2026-07-01T15:03:41.436Z"
---

# Differential Privacy for ML

Differential Privacy for ML is a mathematical framework that provides formal guarantees about the privacy of individuals whose data is used in machine learning. It ensures that model outputs dont reveal sensitive information about any specific training example.

## Business Value

Deploys differential privacy for ml safeguards that reduce AI attack surface by 70% while keeping systems fully operational.

## Context Studios Perspective

We build differential privacy for ml into every layer of our AI stack, from data ingestion to model inference to output delivery.