ML and Privacy Building Secure Data Models: Ensure privacy in ML with federated learning techniques
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Imported Edition - Ships in 18-21 Days
Free Shipping in India on orders above Rs. 500
Protecting user data shouldn't slow down machine learning. ML and Privacy: Building Secure Data Models gives engineers, data scientists, and privacy teams a practical playbook for training and deploying models while keeping sensitive information safe. Starting from first principles-data minimization, threat models, and risk scoring-you'll implement federated learning, differential privacy, and secure aggregation with clear examples and checklists you can apply immediately.
You'll learn how to pick privacy budgets you can explain, reduce leakage risks, and measure utility so stakeholders understand trade-offs. The book closes with pipelines and evidence: logging, consent signals, retention limits, and audits that stand up to scrutiny.
What you'll learn
Map data flows and apply minimization, pseudonymization, and purpose limits
Train at the edge with federated learning and server-side secure aggregation
Use differential privacy (ε/δ) with practical budgeting and utility checks
Reduce exposure with split learning, TEEs/confidential computing (overview)
Prevent leakage: membership inference, inversion, and reconstruction defenses
Govern features, labels, and releases with versioning and lineage
Operate with evidence: consent tracking, retention, redaction, and audits
Who it's for
ML engineers, data scientists, privacy engineers, architects, and product leaders who need privacy-preserving results without derailing delivery.
What's inside
Copy-ready patterns, policy-to-pipeline templates, risk rubrics, and lightweight dashboards for privacy/utility balance.
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