---

name: ds-privacy-ml compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Implements privacy-preserving machine learning including differential privacy, federated learning, and privacy attack prevention"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-data-privacy, ds-model-fairness, ds-reproducible-research role: implementation scope: implementation triggers: privacy machine learning, differential privacy, federated learning, privacy attacks, privacy-preserving archetypes:
  • tactical
  • generation anti_triggers:
  • brainstorming
  • vague ideation
  • code golf
  • over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational version: "1.0.0"

---

Privacy in ML

Comprehensive guide to privacy in ml in machine learning and data science workflows.

When to Use This Skill

• Solving real-world reproducibility & responsible ai problems
• Building machine learning pipelines with privacy in ml
• Implementing best practices for privacy in ml
• Optimizing model performance using privacy in ml techniques
• Learning industry-standard approaches to privacy in ml

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require privacy in ml rigor
• When domain expertise in specific problem requires different approach
• If your problem doesn't require the complexity this skill provides

Purpose and Key Concepts

Privacy in ML is a critical component of the machine learning workflow. This skill covers:

1. Theoretical foundations — Mathematical principles and statistical concepts
2. Practical implementation — Working code examples and patterns
3. Common pitfalls — Mistakes to avoid and how to recover from them
4. Best practices — Industry-standard approaches and optimization techniques

Core Workflow

1. Understand the problem — Clearly define what you're solving for
2. Select approach — Choose the right technique for your data and constraints
3. Implement solution — Write clean, tested code following best practices
4. Validate results — Verify your implementation with tests and validation
5. Optimize performance — Improve efficiency and accuracy incrementally

Implementation Patterns

Pattern 1: Basic Privacy in ML

import numpy as np
from typing import Tuple

def apply_laplace_mechanism(data: np.ndarray, sensitivity: float, epsilon: float) -> np.ndarray:
    """
    Apply the Laplace mechanism to add differential privacy noise to aggregated data.
    
    Args:
        data: Array of numerical values to be privatized
        sensitivity: Maximum change in output caused by a single record
        epsilon: Privacy budget parameter
        
    Returns:
        Privatized array with added Laplace noise
    """
    if epsilon <= 0:
        raise ValueError("Epsilon must be positive")
        
    scale: float = sensitivity / epsilon
    noise: np.ndarray = np.random.laplace(loc=0.0, scale=scale, size=data.shape)
    return data + noise

# Example usage with synthetic dataset
if __name__ == "__main__":
    np.random.seed(42)
    sensitive_data: np.ndarray = np.random.normal(loc=50, scale=10, size=1000)
    original_mean: float = np.mean(sensitive_data)
    privatized_mean: float = np.mean(apply_laplace_mechanism(sensitive_data, sensitivity=1.0, epsilon=1.0))
    print(f"Original Mean: {original_mean:.4f}")
    print(f"Privatized Mean: {privatized_mean:.4f}")

Pattern 2: Production-Ready Privacy in ML

import numpy as np
from typing import List, Dict, Any
import logging

logger = logging.getLogger(__name__)

class FederatedPrivacyTrainer:
    """Production-grade federated learning with differential privacy guarantees."""
    
    def __init__(self, base_model: Any, epsilon: float = 1.0, delta: float = 1e-5, clip_norm: float = 1.0):
        self.base_model = base_model
        self.epsilon = epsilon
        self.delta = delta
        self.clip_norm = clip_norm
        self.global_weights: np.ndarray | None = None
        
    def _clip_and_noise(self, weights: np.ndarray) -> np.ndarray:
        """Clip weights and add Gaussian noise for (epsilon, delta)-DP."""
        norm: float = np.linalg.norm(weights)
        if norm > self.clip_norm:
            weights = weights * (self.clip_norm / norm)
        scale: float = self.clip_norm * np.sqrt(2 * np.log(1.25 / self.delta)) / self.epsilon
        noise: np.ndarray = np.random.normal(0, scale, weights.shape)
        return weights + noise
        
    def aggregate_round(self, client_updates: List[np.ndarray]) -> np.ndarray:
        """Aggregate client updates with DP noise."""
        if not client_updates:
            raise ValueError("No client updates provided")
            
        avg_weights: np.ndarray = np.mean(client_updates, axis=0)
        return self._clip_and_noise(avg_weights)
        
    def train_round(self, client_weights: List[np.ndarray]) -> Dict[str, Any]:
        """Execute one round of federated training."""
        logger.info(f"Aggregating {len(client_weights)} client updates")
        self.global_weights = self.aggregate_round(client_weights)
        return {
            "status": "success"
            "round_weights": self.global_weights
            "privacy_budget": {"epsilon": self.epsilon, "delta": self.delta}
        }

BAD vs GOOD: Privacy in ML

# BAD: Ignoring privacy budget and clipping gradients
def train_bad(X: np.ndarray, y: np.ndarray, lr: float = 0.1, epochs: int = 10) -> np.ndarray:
    w: np.ndarray = np.zeros(X.shape[1])
    for _ in range(epochs):
        pred: np.ndarray = 1 / (1 + np.exp(-X @ w))
        grad: np.ndarray = X.T @ (pred - y) / len(y)
        w -= lr * grad  # No clipping, no noise, no epsilon tracking
    return w

# GOOD: Proper DP-SGD with clipping, noise, and budget tracking
def train_good(X: np.ndarray, y: np.ndarray, lr: float = 0.1, 
               epochs: int = 10, epsilon: float = 1.0, delta: float = 1e-5) -> np.ndarray:
    w: np.ndarray = np.zeros(X.shape[1])
    n: int = len(y)
    for _ in range(epochs):
        idx: np.ndarray = np.random.permutation(n)
        X_b: np.ndarray = X[idx]
        y_b: np.ndarray = y[idx]
        pred: np.ndarray = 1 / (1 + np.exp(-X_b @ w))
        grad: np.ndarray = X_b.T @ (pred - y_b) / n
        
        # Gradient clipping
        norm: float = np.linalg.norm(grad)
        if norm > 1.0:
            grad *= 1.0 / norm
            
        # DP noise injection
        scale: float = lr * np.sqrt(2 * np.log(1.25 / delta)) / epsilon
        grad += np.random.normal(0, scale, grad.shape)
        
        w -= lr * grad
    return w

Best Practices

• ✅ Always validate your implementation on test data
• ✅ Document your assumptions and methodology
• ✅ Use version control for reproducibility
• ✅ Monitor performance metrics in production
• ✅ Periodically review and update your approach
• ✅ Test with edge cases and outliers
• ✅ Log all significant operations for debugging
• ✅ Follow NIST AI RMF and OpenDP guidelines for privacy budget accounting

Common Pitfalls

| Pitfall | Problem | Solution | |

---

---

Constraints

MUST DO

• Validate all data preprocessing steps are fit-only on training data, never on validation or test sets
• Implement reproducible pipelines with fixed random seeds and deterministic operations where possible
• Report model performance with confidence intervals via bootstrapping or cross-validation across multiple runs
• Log all experiments with parameters, metrics, and artifacts using MLflow or equivalent tracking system

MUST NOT DO

• Do not evaluate a model on the same data used for training — always hold out a proper test set
• Avoid overfitting to the validation set by limiting hyperparameter search iterations
• Never use features that can only be computed at inference time (look-ahead bias)
• Do not report single-run accuracy without statistical significance testing or error bars

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Differential Privacy — Wikipedia
• OpenDP Documentation
• TensorFlow Privacy
• PyTorch Opacus Documentation
• Privacy Budget Accounting (NIST AI RMF)