Differential Privacy
If you like applying Differential Privacy, every challenge here gives you a chance to practice it on a real industry brief.
- CodeIntermediateNew
Add Differential Privacy to a Mental-Health App's Analytics Dashboard
Wrap the analytics module in `analytics_module.py` (provided) with a differential-privacy layer built on OpenDP. Implement epsilon-delta accounting: add Laplace noise to counts …
- Differential Privacy
- Privacy Budget
- Python Programming
Open coursework - CodeIntermediateNew
Generate Synthetic Tabular Data with Privacy Guarantees
Implement DP synthetic data generation: either DP-CTGAN, PATE-GAN, or a marginal-based DP method like PrivBayes / MWEM. Train on the real dataset (around 200,000 transactions, 1…
- Synthetic Data
- Differential Privacy
- Generative Models
Privacy-Preserving Machine Learning - CodeIntermediateNew
Implement Federated Learning for a Government Statistics Office
Use Flower as the FL framework. Simulate 8 municipalities each with a partition of a synthetic wage dataset (provided, 1M rows, EU-Labour-Force-Survey schema). Train a gradient-…
- Federated Learning
- Differential Privacy
- Python Programming
Privacy-Enhancing Technologies - StrategyIntermediateNew
Design a PETs Strategy for an EU AI Act Use Case
Map the underwriting use case to applicable PETs across the data-lifecycle stages (training, evaluation, inference, monitoring). For each, document: privacy property gained, acc…
- Pets Strategy
- Differential Privacy
- Federated Learning
Open coursework Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- CodeIntermediateNew
Train a Differentially Private Classifier on Medical Records
Use Opacus (PyTorch DP-SGD library). Train a tabular classifier (small MLP + gradient-boosted features) with DP-SGD at the agreed epsilon/delta. Run an accuracy-vs-privacy front…
- Differential Privacy
- Dp Sgd
- Opacus
Open coursework
How it works
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Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
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Step 06
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