Mathematical Privacy Guarantee
Differential Privacy provides a mathematical guarantee that the output of an algorithm is nearly identical whether or not any individual's data is included.
Pr[M(D₁) ∈ S] ≤ e^ε × Pr[M(D₂) ∈ S]
🧠 Intuitive Understanding
If Alice's data is in the dataset or not, an attacker shouldn't be able to tell the difference from the AI model's behavior. The smaller ε (epsilon), the stronger the privacy protection!
⚖️ Privacy-Utility Tradeoff
Stronger privacy (smaller ε) means adding more noise, which can reduce model accuracy. Finding the right balance is crucial for practical applications.
DP Mechanisms
Implementation Methods for differential privacy in deep learning:
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📊
Laplace Mechanism
Add noise proportional to sensitivity and ε
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Gaussian Mechanism
Add Gaussian noise for (ε,δ)-differential privacy
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🎯
DP-SGD
Differentially private stochastic gradient descent
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Private Aggregation
Securely combine gradients with privacy guarantees