← Back to glossary
Data, Consent & Privacy
4 min read
Differential privacy is a mathematical technique that adds carefully calibrated noise to data or results, so useful patterns remain visible while no single individual can be identified. It lets advertisers learn from data in aggregate without exposing any one person within it.
Start with real data. The underlying dataset holds individual records.
Add calibrated noise. Randomness is introduced in a controlled way.
Preserve the pattern. Aggregate trends stay accurate enough to use.
Protect the individual. No single person can be reverse-engineered out.
Aggregated reporting. Sharing trends without exposing people.
Measurement. Privacy-safe conversion and audience data.
Analytics. Learning from data at the group level.
Platform APIs. Underpinning privacy-preserving ad tools.
Strong guarantee. It offers a provable privacy protection.
Utility with privacy. Data stays useful while people stay safe.
Future-fit. It suits a world of tighter data rules.
Accuracy trade-off. More privacy means noisier results.
Complexity. It is technically demanding to apply well.
Tuning. Calibrating the noise is a delicate balance.
Understanding. Its guarantees are easy to misinterpret.
Keep exploring
Browse all 61 advertising terms
→
OpenAds connects your assistant to every major ad platform, so you can plan, launch and optimise campaigns in plain language — approving the moves that matter.
Explore OpenAds