Cryptographic Differential Privacy Lab
Enterprise Autonomous Agent Differential Privacy & Gradient Sanitizer
Simulate and benchmark autonomous agent Differential Privacy (DP-SGD) and gradient inversion leakage. Calculate epsilon privacy budgets, gradient clipping, Gaussian noise injection, and DPDP Act compliance to eliminate Deep Leakage from Gradients (DLG).
Differential Privacy Defense Architecture Comparison
- Raw Unsanitized Backprop: Epsilon = infinity, 98.6% gradient inversion reconstruction risk, non-compliant with DPDP Act 2023.
- Naive Laplace Noise: Epsilon = 8.5, 48.2% inversion risk, unclipped outlier gradients leak sensitive PII tokens.
- Elesium DP-SGD Gaussian Sanitizer: Epsilon = 1.2 (δ = 10^-5), <1.8% inversion risk, 98.4% model utility retention, 100% DPDP Act Section 8(4) and HIPAA compliance.