Add MDP (Matrix Decomposition + Differential Privacy) defense

examples/defense/MDP.py

@@ -0,0 +1,100 @@
+"""
+MDP Defense Example
+
+Demonstrates the Matrix Decomposition + Differential Privacy defense
+for protecting GNN models against privacy attacks.
+
+Usage:
+    python examples/defense/MDP.py
+"""
+
+from pygip.datasets import Cora, CiteSeer, PubMed
+from pygip.models.defense import MDP
+
+
+def main():
+    # Load dataset with PyG format (required for MDP)
+    print("Loading Cora dataset...")
+    dataset = Cora(api_type='pyg')
+
+    print(f"Dataset: {dataset.dataset_name}")
+    print(f"Nodes: {dataset.num_nodes}")
+    print(f"Features: {dataset.num_features}")
+    print(f"Classes: {dataset.num_classes}")
+
+    # Initialize MDP defense
+    print("\nInitializing MDP defense...")
+    defense = MDP(
+        dataset=dataset,
+        attack_node_fraction=0.1,
+        # MDP parameters
+        nc=4,               # Number of federated calculators
+        es=2,               # Number of eigenvalue shares
+        epsilon=30.0,       # DP privacy budget (higher = less noise)
+        keep_ratio=0.8,     # Training data fraction per calculator
+        # Model architecture
+        hidden_dim=16,
+        dropout=0.5,
+        # Training
+        lr=0.01,
+        weight_decay=5e-4,
+        epochs=200,
+        patience=50,
+        seed=42,
+    )
+
+    # Execute defense
+    print("\nExecuting MDP defense...")
+    results = defense.defend()
+
+    # Print results
+    print("\n" + "=" * 50)
+    print("DEFENSE RESULTS")
+    print("=" * 50)
+
+    for key, value in results.items():
+        if isinstance(value, float):
+            print(f"  {key}: {value:.4f}")
+        else:
+            print(f"  {key}: {value}")
+
+    # Access internal state if needed
+    print("\nDefense Details:")
+    print(f"  Adjacency shares: {len(defense.get_adjacency_shares())}")
+    stats = defense.get_training_stats()
+    if stats:
+        print(f"  Training epochs: {stats['epochs_trained']}")
+        print(f"  Final val accuracy: {stats['val_acc'][-1]:.4f}")
+
+
+def run_epsilon_sweep():
+    """Sweep over different privacy budgets."""
+    print("\n" + "=" * 50)
+    print("EPSILON SWEEP")
+    print("=" * 50)
+
+    dataset = Cora(api_type='pyg')
+
+    epsilons = [10.0, 20.0, 30.0, float('inf')]
+
+    for eps in epsilons:
+        defense = MDP(
+            dataset=dataset,
+            nc=4,
+            es=2,
+            epsilon=eps,
+            epochs=100,
+            patience=30,
+            seed=42,
+        )
+
+        results = defense.defend()
+        acc = results.get('test_acc', 0)
+
+        eps_str = "inf" if eps == float('inf') else f"{eps:.1f}"
+        print(f"epsilon={eps_str:>6}: test_acc={acc:.4f}")
+
+
+if __name__ == "__main__":
+    main()
+    run_epsilon_sweep()