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differential-privacy

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Experiments at the intersection of ML security & privacy: adversarial attacks/defenses (FGSM/PGD, adversarial training), differential privacy (DP-SGD, ε–δ), federated learning privacy (secure aggregation), and auditing (membership/model inversion). PyTorch notebooks + eval scripts.

  • Updated Oct 15, 2025
  • Jupyter Notebook

Advanced privacy toolkit for phones, devices, and signals. Hybrid AES-256-GCM with chaotic keystreams, Laplace differential privacy, reversible FFT spectrum scrambling, LWE-inspired lattice noise, and entropy-based leakage detection. All techniques grounded in published research with executable roundtrips and CLI.

  • Updated Aug 21, 2026
  • Python

Adaptive Federated Learning Framework (AFLF): a distributed ML systems project implementing dynamic client selection, differential privacy, adaptive optimization, and communication-efficient federated training with reproducible experiments and a Streamlit research dashboard.

  • Updated Apr 8, 2026
  • Python

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