Loading patterns…
Differential Privacy Patterns(DPP)
Privacy-preserving data processing with mathematical privacy guarantees
In 30 seconds
- What
- Adds calibrated noise to query results so changing any single record minimally affects output, with formal privacy guarantees measured by epsilon.
- When to use
- Aggregating sensitive data where you must prove no individual's presence or absence can be reliably inferred from results.
- Watch out
- Smaller epsilon (stronger privacy) drastically reduces result accuracy; tuning requires deep statistical expertise and domain knowledge of acceptable utility loss.
Ask the AI expert about this pattern
Opens the assistant with your question prefilled. You review it before sending.
Differential Privacy Patterns: Overview
Privacy-preserving data processing with mathematical privacy guarantees
- Mathematical privacy guarantees
- Noise injection mechanisms
- Privacy budget management
- Utility-privacy trade-off optimization
- Composition theorem application
- Formal privacy analysis
The Agent Architect
One pattern, one tradeoff, one production failure story. A short weekly briefing for people building agentic systems.
Weekly email, one-click unsubscribe. We only use your address to send the briefing.
References
The papers, specifications, and repositories this pattern is based on.
- Apple Differential Privacy (2024)
- Google Privacy Research
From the engineer behind this catalog
Get your agent system red-teamed
The controls described here only hold if somebody tries to break them. Have yours tested the way a real attacker would: prompt injection, jailbreaks, tool misuse and data exfiltration, every finding written up next to its fix.
€750 instead of €1,500, one week, written report and walkthrough call, until 30 September