Synthetic Data Generation: Balancing Utility and Privacy

My journey experimenting with synthetic datasets for real-world use cases.

Maximilian Oliver

3 min read

·

Sep 27, 2025

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Synthetic data has become one of the most exciting areas in data science. Whether for training ML models, testing systems, or sharing data without privacy risks, synthetic datasets give us a way to work around restrictions. In this article, I’ll share my experience building, evaluating, and applying synthetic data.

1. Why Synthetic Data?

Real-world data comes with challenges:

• Privacy concerns (e.g., healthcare, finance).
• Limited size or imbalance.
• Accessibility barriers.

Synthetic data solves this by simulating real datasets while maintaining structure and statistical patterns.

import pandas as pd

real_data = pd.DataFrame({
    "age": [22, 35, 44, 51],
    "income": [2500, 4000, 5200, 6000]
})
print(real_data)

2. Simple Rule-Based Generation

The simplest approach: define rules and randomly generate values.

import numpy as np

synthetic_data = pd.DataFrame({
    "age": np.random.randint(20, 60, size=100),
    "income"…