Differential Privacy Libraries

Differential Privacy Libraries

Differential privacy provides mathematical guarantees about individual privacy in datasets. Libraries implementing differential privacy algorithms enable developers to build systems that learn from user data without compromising individual privacy. These tools are essential for analytics, machine learning, and research applications handling sensitive data.

Google's Differential Privacy Library offers production-ready implementations in C++, Go, and Java. It includes algorithms for computing counts, sums, means, and percentiles with calibrated noise addition. The library handles the complex mathematics of privacy budget management and noise calibration.

# Using Google's Python Differential Privacy library
from pydp.algorithms.laplacian import BoundedSum, BoundedMean, Count
import pandas as pd

class DifferentialPrivacyAnalyzer:
    def __init__(self, epsilon=1.0, delta=1e-5):
        self.epsilon = epsilon  # Privacy budget
        self.delta = delta      # Failure probability
        
    def analyze_user_data(self, data):
        """Analyze user data with differential privacy guarantees"""
        results = {}
        
        # Count users with DP
        counter = Count(epsilon=self.epsilon/4)
        results['user_count'] = counter.quick_result(data)
        
        # Average age with DP (bounded between 0-120)
        age_mean = BoundedMean(
            epsilon=self.epsilon/4,
            lower_bound=0,
            upper_bound=120
        )
        for user in data:
            age_mean.add_entry(user['age'])
        results['average_age'] = age_mean.result()
        
        # Total revenue with DP (bounded between 0-1000000)
        revenue_sum = BoundedSum(
            epsilon=self.epsilon/4,
            lower_bound=0,
            upper_bound=1000000
        )
        for user in data:
            revenue_sum.add_entry(user['revenue'])
        results['total_revenue'] = revenue_sum.result()
        
        # Histogram with DP
        results['age_distribution'] = self.create_private_histogram(
            [user['age'] for user in data],
            bins=[0, 18, 25, 35, 45, 55, 65, 100],
            epsilon=self.epsilon/4
        )
        
        return results
    
    def create_private_histogram(self, data, bins, epsilon):
        """Create histogram with differential privacy"""
        histogram = {}
        
        for i in range(len(bins) - 1):
            bin_name = f"{bins[i]}-{bins[i+1]}"
            count = Count(epsilon=epsilon/len(bins))
            
            # Count items in this bin
            for value in data:
                if bins[i] <= value < bins[i+1]:
                    count.add_entry(1)
            
            histogram[bin_name] = count.result()
        
        return histogram

# JavaScript implementation using differential-privacy library
class DifferentialPrivacyJS {
  constructor(epsilon = 1.0) {
    this.epsilon = epsilon;
  }
  
  addLaplaceNoise(value, sensitivity) {
    // Add Laplace noise for differential privacy
    const scale = sensitivity / this.epsilon;
    const u = Math.random() - 0.5;
    const noise = -scale * Math.sign(u) * Math.log(1 - 2 * Math.abs(u));
    return value + noise;
  }
  
  privateCount(data, predicate) {
    // Count with differential privacy
    const trueCount = data.filter(predicate).length;
    const sensitivity = 1; // Each person can affect count by at most 1
    return Math.max(0, Math.round(
      this.addLaplaceNoise(trueCount, sensitivity)
    ));
  }
  
  privateAverage(data, accessor, bounds) {
    // Average with differential privacy
    const values = data.map(accessor);
    const clippedValues = values.map(v => 
      Math.max(bounds.min, Math.min(bounds.max, v))
    );
    
    const sum = clippedValues.reduce((a, b) => a + b, 0);
    const count = clippedValues.length;
    
    // Add noise to sum and count separately
    const noisySum = this.addLaplaceNoise(
      sum, 
      bounds.max - bounds.min
    );
    const noisyCount = this.addLaplaceNoise(count, 1);
    
    return noisySum / Math.max(1, noisyCount);
  }
}

Microsoft's SmartNoise SDK provides a higher-level interface for differential privacy, including SQL query interfaces and automatic privacy budget management. It's particularly useful for organizations wanting to provide differentially private data access without requiring deep expertise in the underlying mathematics.