repoGitHubTrust 82 · PrimaryPublished 3d agoLive · 3d ago
meta-pytorch/opacus
Training PyTorch models with differential privacy
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 60%EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy →
- PossiblePossibly related (embedding) · 57%TabPATE: Differentially Private Tabular In-Context Learning Without Public Data →
- PossiblePossibly related (embedding) · 56%Dithered Gaussian Mechanism for Randomness-Efficient Differential Privacy →
- PossiblePossibly related (embedding) · 54%Amplifying Membership Signal Through Chained Regeneration →
- PossiblePossibly related (embedding) · 52%Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP →
Implements
paperEdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential PrivacypaperTabPATE: Differentially Private Tabular In-Context Learning Without Public DatapaperDithered Gaussian Mechanism for Randomness-Efficient Differential PrivacypaperAmplifying Membership Signal Through Chained Regeneration
Covers
Related across the graph
paperTabPATE: Differentially Private Tabular In-Context Learning Without Public DatanewsProfiling in PyTorch (Part 2): From nn.Linear to a Fused MLPpaperEdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential PrivacypaperDithered Gaussian Mechanism for Randomness-Efficient Differential PrivacypaperAmplifying Membership Signal Through Chained Regeneration