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Data Privacy

Experiments

K-Anonymity

  • Samarati

  • Mondrian

Privacy Preserving Federated Learning

  • Differential Privacy

    • Gaussian Mechanism

  • Secure Multi-Party Computation

    • Paillier Homomorphic Encryption

Detailed reports are inside the two directories.

Assignments

HW1: Basics of Privacy

  • (c, l)-diversity

  • quasi-identifier & full domain generalization & suppression

  • t-closeness & Earth Mover's distance

  • k-anonymity

  • prior and posterior probabilities & mutual information

  • (α, β)-privacy & γ-amplifying

HW2: Differential Privacy (DP)

  • (ε, δ)-DP

  • local DP

  • sensitivity; laplace, gussian, and exponential mechanisms

  • composition theorem & advanced composition theorem

  • random subsampling

HW3: Cryptography & Security

  • one time pad

  • interchangeble libraries

  • negligible functions

  • pseudo random generator (PRG)

  • RSA