Synthetic Data
A Smart but Tricky Way to Protect Privacy
About
Imagine a company wants to analyze customer trends but can’t use real customer data due to privacy concerns. What if they could create a new dataset that behaves just like the real data—but without exposing anyone’s personal information?
That’s the idea behind synthetic data (SD). Instead of using actual sensitive data, SD generates new data that reflects the same trends and patterns.
But let’s break this down in a simple way.
How Does Synthetic Data Work?
Think of a company database containing employee details — like date of birth, job title, and salary. This information is sensitive, so sharing it directly could violate privacy laws.
One way to get around this is to use mock data, which just creates random records that look correct (e.g., names that start with capital letters and salaries in dollars). But mock data doesn’t reflect reality — it might generate an unrealistic combination like a student earning $12,000 a month.
Synthetic data is different — instead of making random entries, it learns patterns from the real data and generates records that follow the same rules. So, instead of creating a random salary, synthetic data would recognize that a VP of Engineering typically earns $12,000/month, making the dataset realistic without exposing real people’s information.
This approach allows businesses to analyze data safely while still getting meaningful insights.
The Challenges of Synthetic Data
While SD sounds like the perfect solution, it comes with two major challenges:
Privacy vs. Accuracy Trade-Off
Since synthetic data is built using real data patterns, there’s a risk that it might accidentallyreveal too much information. If the system isn’t careful, it could recreate areal person’s unique datawithout realizing it.
For example, if someone has arare combination of job title, birth year, and salary, an attacker might be able tore-identify themby analyzing the synthetic dataset—a type of security risk called amembership inference attack.
At the same time, if the system triestoo hardto hide individual details, the data may becomeless usefulbecause it no longer reflects real-world trends. Striking the right balance betweenprivacy and accuracyis one of the biggest challenges in synthetic data.Bias and Data Quality Issues
Synthetic data can only be asgoodas the original dataset it’s based on. If the real data isincomplete, biased, or low-quality, the synthetic data willinherit those same problems.
For example, inmedical imaging, different types of scans (X-rays, MRIs, CTs) are used for different medical conditions. But becauseMRIs are more expensive, there may be fewer MRI scans in the dataset. If the synthetic data is based onan unbalanced dataset, it might generatemore fake X-rays than MRIs, making the data less useful for certain medical research.
When multiple types of data are involved (text, images, numbers), these issuesmultiply, making it harder to create a high-quality synthetic dataset.
Is Synthetic Data the Right Solution?
Synthetic data is a powerful tool, but it’s not always the best option. If the goal is to get a general sense of what data looks like, then mock data might be enough. But if businesses need to train AI models, analyze trends, or make predictions, synthetic data can be a great way to do so without using real people’s private information.
However, businesses must be careful:
Too accurate?You risk revealing real data.Too private?The data becomes meaningless.
Synthetic data can be useful, but only if it’s carefully designed to maintain both privacy and quality. Otherwise, companies may end up with data that’s either too revealing or too useless — and neither is a good outcome.