Why privacy-preserving synthetic data is a key tool for businesses

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The tangible world we were born into is becoming more and more homogenized with the digital world we have created. Gone are the days when your most sensitive information, like your social security number or bank details, was simply locked away in a safe in your bedroom closet. Now, private data can become vulnerable if not handled properly.

That's the problem we face today in the landscape populated by career hackers whose full-time jobs are to dip into your data feeds and steal your identity, money, or confidential information.

While digitization has allowed us to make great strides, it also presents new privacy and security issues, even for data that is not entirely "real".

In fact, the advent of synthetic data to inform AI processes and streamline workflows has been a huge leap forward across many verticals. But synthetic data, like real data, is not as generalized as one might think.

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Synthetic data is believed to consist of information produced by models of real data. It is a statistical prediction from real data that can be mass generated. Its main application is to inform AI technologies so that they can perform their functions more efficiently.

Like any model, AI can discern real events and generate data based on historical data. The Fibonacci sequence is a classic mathematical model where each number in the sequence adds the previous two numbers in the sequence to derive the next number. For example, if I give you the sequence "1,1,2,3,5,8", a trained algorithm could guess the next numbers in the sequence based on the parameters I set.

This is actually a simplified, abstract example of synthetic data. If the parameter is that each following number must be equal to the sum of the previous two numbers, then the algorithm must render "13, 21, 34" and so on. The last sentence of numbers is the synthetic data deduced by the AI.

Businesses can collect limited but powerful data about their audience and customers and set their own parameters to create synthetic data. This data can inform all AI-driven business activities, such as improving sales technology and increasing satisfaction with product feature requests. It can even help engineers anticipate future faults in machines or programs.

There are countless applications for synthetic data, and they can often be more useful than the real data from which they come.

If it's fake data, it has to be safe, right?

Not quite. As intelligently as synthetic data is created, it can just as easily be reverse-engineered to extract personal data from the real-world samples used to make it. This can unfortunately become the front door hackers need to find, manipulate and collect the personal information of sample users.

This is where...