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Category: Protocols & Standards
Category: Protocols & Standards
Differential Privacy is a mathematical framework and standard for protecting individual privacy in datasets while enabling useful AI model training and analysis.
Differential Privacy adds carefully calibrated noise to data or model updates, ensuring individual records cannot be identified. It provides mathematical guarantees about privacy loss.
The technique balances privacy protection with model utility through a privacy budget that limits information disclosure.
Differential Privacy enables AI training on sensitive data like medical records or financial information. It's becoming a regulatory requirement in many jurisdictions.
Tech giants use differential privacy to improve products while protecting user privacy, setting industry standards.
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