Privacy-Preserving AI - Differential Privacy & Federated Learning

# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# DIFFERENTIAL PRIVACY - mathematical privacy guarantee
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

# PRIVACY ATTACKS ON ML MODELS:
privacy_attacks = {
    "Membership inference": "Given a model, determine if a specific sample was in training data",
    "Model inversion":      "Reconstruct training data from model predictions (faces from face recognition model)",
    "Data extraction":      "Prompt engineering to extract memorized training text from LLMs",
    "Attribute inference":  "Infer sensitive attributes (health status, income) from model predictions",
}

# DIFFERENTIAL PRIVACY: Adding calibrated noise during training
# Formal guarantee: presence/absence of any single person's data
# changes model behavior by at most factor e^epsilon

# Epsilon (privacy budget):
# epsilon < 1: very high privacy, significant utility loss
# epsilon = 1-10: reasonable privacy, small utility loss
# epsilon > 10: mostly theoretical, weak practical guarantee

# DP-SGD (Differentially Private Stochastic Gradient Descent):
# 1. Compute per-sample gradients (instead of batch average)
# 2. Clip each sample's gradient to bound sensitivity
# 3. Add Gaussian noise scaled to clipping + privacy budget
# 4. Take the average noisy gradient as the update

# pip install opacus  (PyTorch DP library from Meta)
from opacus import PrivacyEngine
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset

model = nn.Sequential(nn.Linear(20, 64), nn.ReLU(), nn.Linear(64, 2))
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

# Synthetic dataset
X = torch.randn(1000, 20)
y = torch.randint(0, 2, (1000,))
loader = DataLoader(TensorDataset(X, y), batch_size=64, shuffle=True)

privacy_engine = PrivacyEngine()
model_dp, optimizer_dp, loader_dp = privacy_engine.make_private(
    module=model,
    optimizer=optimizer,
    data_loader=loader,
    noise_multiplier=1.1,    # how much noise to add (larger = more private, worse accuracy)
    max_grad_norm=1.0,       # gradient clipping bound (sensitivity)
)

for epoch in range(5):
    for X_batch, y_batch in loader_dp:
        optimizer_dp.zero_grad()
        nn.CrossEntropyLoss()(model_dp(X_batch), y_batch).backward()
        optimizer_dp.step()

    epsilon = privacy_engine.get_epsilon(delta=1e-5)
    print(f"Epoch {epoch+1}: epsilon = {epsilon:.2f}")
# Lower epsilon = stronger privacy = slightly lower accuracy

# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# FEDERATED LEARNING -- train without centralizing data
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# pip install flwr (Flower -- federated learning framework)
import flwr as fl
import torch
from collections import OrderedDict

class FederatedClient(fl.client.NumPyClient):
    '''Each hospital/phone/organization is one client.'''
    def __init__(self, model, local_train_data, local_val_data):
        self.model = model
        self.train_data = local_train_data
        self.val_data = local_val_data

    def get_parameters(self, config):
        '''Return current model weights to server.'''
        return [val.cpu().numpy() for _, val in self.model.state_dict().items()]

    def set_parameters(self, parameters):
        '''Update model with aggregated weights from server.'''
        params_dict = zip(self.model.state_dict().keys(), parameters)
        state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
        self.model.load_state_dict(state_dict, strict=True)

    def fit(self, parameters, config):
        '''Train locally for a few epochs, NEVER share raw data.'''
        self.set_parameters(parameters)  # receive global model
        self.model.train()
        optimizer = torch.optim.Adam(self.model.parameters(), lr=1e-3)

        for _ in range(config.get("local_epochs", 3)):
            for X, y in self.train_data:
                optimizer.zero_grad()
                nn.CrossEntropyLoss()(self.model(X), y).backward()
                optimizer.step()

        # Return: updated weights (not data!) + number of samples trained on
        return self.get_parameters(config={}), len(self.train_data.dataset), {}

    def evaluate(self, parameters, config):
        '''Evaluate local model on local data.'''
        self.set_parameters(parameters)
        self.model.eval()
        loss = accuracy = 0
        with torch.no_grad():
            for X, y in self.val_data:
                out = self.model(X)
                loss += nn.CrossEntropyLoss()(out, y).item()
                accuracy += (out.argmax(1) == y).float().mean().item()

        n = len(self.val_data)
        return loss/n, len(self.val_data.dataset), {"accuracy": accuracy/n}

# FedAvg: server aggregates client weights by weighted average
# 1. Server sends current global model to all clients
# 2. Each client trains locally for 3 epochs on their private data
# 3. Clients send ONLY updated weights back (data never leaves)
# 4. Server computes weighted average of all clients' weights
# 5. Updated global model sent to next round of clients

# Real-world use cases:
federated_use_cases = {
    "Keyboard autocomplete":    "Google Gboard -- learns your typing patterns on-device",
    "Medical AI":               "Hospitals train on patient data without sharing records across institutions",
    "Fraud detection":          "Banks collaborate on fraud models without sharing customer transactions",
    "Autonomous vehicles":      "Cars share driving patterns without uploading personal identifiable routes",
}

for case, example in federated_use_cases.items():
    print(f"  {case:30s}: {example}")