Why should we use synthetic data? :3 Advantages & 3 Disadvantages

Synthetic data is newly created data that mimics the characteristics of original data, providing an important alternative that replicates the traits of existing datasets while solving issues such as the protection of personal information.

The advancement of artificial intelligence and machine learning has enabled the creation of realistic and diverse new data. This contributes to solving the problems of data scarcity and reducing bias.

Here, we will explore the pros and cons.

Advantages of Synthetic Data

1. Privacy Protection:

In medical research, for example, it can protect patient privacy while facilitating important studies.

2. Improved Data Accessibility

It can be used in areas where data collection is costly or time-consuming, such as financial market analysis.

3. Increased Diversity and Inclusivity

Helps address data scarcity and bias, for example, by creating diverse road conditions for autonomous vehicle training.

Disadvantages of Synthetic Data

1. Accuracy and Reliability Issues

There’s a risk that this data may not accurately mirror actual datasets, potentially leading to erroneous results in predictive modeling.

2. Overfitting Risk

Training models solely on synthetic data may reduce their performance in real-world scenarios.

3. Ethical Considerations

The use of real data to create synthetic versions raises privacy and consent issues.

Overall, while synthetic data offers many benefits, attention to accuracy, reliability, and ethical concerns is crucial. Continuous validation and improvement are essential to maximize its potential.

If you’re interested in CUBIG’s vision, Learn more through the link below!

CUBIG’s innovation in Differential Privacy : A Global Breakthrough