[Verse 1] Data whispers secrets through encrypted halls Algorithms hunger for personal details But guardians stand with differential shields Adding noise that never truly reveals Epsilon parameters set the boundary Between utility and anonymity [Chorus] Privacy preserved, data governed well Differential noise breaks the spell Federated learning keeps it local Synthetic data makes it vocal GDPR and CCPA align When AI systems cross the line [Verse 2] Scattered clients train their models separately Never sharing raw data centrally Gradients aggregate without exposure Each device maintains its own composure PII detectors scan the pipeline streams Redacting names from training schemes [Chorus] Privacy preserved, data governed well Differential noise breaks the spell Federated learning keeps it local Synthetic data makes it vocal GDPR and CCPA align When AI systems cross the line [Bridge] Article Twenty-Two demands explanation Automated decisions need justification Purpose limitation guides collection Consent management requires reflection Data minimization trims the excess Right to erasure cleans up the mess [Verse 3] Opacus wraps PyTorch with privacy bounds TensorFlow shields where sensitive data's found Synthetic generators mirror true patterns While protecting individuals that matter HIPAA watches healthcare algorithms PIPEDA guards Canadian citizens [Final Chorus] Privacy preserved, data governed well Differential noise breaks the spell Federated learning keeps it local Synthetic data makes it vocal Framework built with regulation spine Where privacy and progress intertwine [Outro] Governance frameworks guide the way Privacy techniques here to stay AI systems learn to respect The data subjects they protect
← Unit 6.3 — AI Security & Adversarial Robustness | Unit 6.5 — AI Governance Frameworks & Organizational Readiness →