How Synthetic Data is Generated

Synthetic Data is a privacy-enhancing technology that creates artificial datasets. It works by learning from the structure and statistical properties of real data. It is designed to preserve analytical utility while protecting individual privacy. The process involves three key stages:

Stage 1: Extract Characteristics from Original Data

The system analyzes the original dataset to understand its structure and relationships:

Stage 2: Generate Fictional Records

Based on the extracted characteristics, synthetic records are created using advanced modeling techniques:

• Generative Algorithms: Generative Adversarial Networks (GANs) or other statistical models.
• Privacy Assurance: These records are entirely fictional and do not correspond to real individuals.

Stage 3: Validate Privacy

The Synthetic Data dataset undergoes rigorous validation to ensure privacy protection:

• Re-identification Risk Analysis: It ensures that no original entries can be inferred or reconstructed.
• Privacy Techniques Applied: It includes methods like privacy risk scoring to quantify and mitigate risks.

Table: Original Dataset

Table: Synthetic Data Dataset