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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, such as your social security number or bank account information, was only locked away in a safe in your bedroom closet. Now private data can become vulnerable if not properly taken care of.
This is the problem we face today in the landscape populated by career hackers whose full-time jobs peck into your data streams and steal your identity, money or proprietary information.
While digitization has helped us make great strides, it also brings new challenges in terms of privacy and security, even for data that isn’t quite ‘real’.
In fact, the advent of synthetic data to inform AI processes and streamline workflows has been a quantum leap in many industries. But synthetic data, like real data, is not as common as you might think.
What are synthetic data and why are they useful?
Synthetic data, as it sounds, is made of information produced by patterns of real data. It is a statistical prediction based on real data that can be generated en masse. Its primary application is to inform AI technologies so that they can perform their functions more efficiently.
Like any pattern, AI can discern real events and generate data based on historical data. The Fibonacci sequence is a classic mathematical pattern 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 can intuitively sense the next numbers in the sequence based on parameters I set.
This is basically a simplified and abstract example of synthetic data. If the parameter is that each subsequent number must be equal to the sum of the previous two numbers, then the algorithm should return “13, 21, 34” and so on. The last set of numbers is the synthetic data derived by the AI.
Companies can collect limited but powerful data about their audiences and customers and set their own parameters to build synthetic data. That data can inform all AI-driven business activities, such as improving sales technology and increasing satisfaction with product feature requirements. It can even help engineers anticipate future machine or program failures.
There are countless uses for synthetic data, and it can often be more useful than the real data it comes from.
If it’s fake data, it should be safe, right?
Not quite. As smart as synthetic data is made, it can just as easily be reversed to extract personal data from the real-world examples used to create it. Unfortunately, this can become the go-to for hackers to find, manipulate, and collect user samples’ personal information.
This is where the issue of securing synthetic data comes into play, especially for data stored in the cloud.
There are many risks associated with cloud computing, all of which can threaten the data that makes up a synthesized dataset. If an API is tampered with or data is lost due to human error, any sensitive information that comes from the synthesized data can be stolen or misused by an attacker. Protecting your storage systems is paramount to preserving not only proprietary data and systems, but also the personal data contained therein.
The important observation to note is that even practical methods of anonymizing data do not guarantee a user’s privacy. There is always the possibility of a loophole or unforeseen hole where hackers can gain access to that information.
Practical steps to improve the privacy of synthetic data
Many data sources used by companies can contain identifying personal information that can compromise users’ privacy. Therefore, data consumers must implement structures to delete personal information from their datasets, as this reduces the risk of sensitive data being revealed to bad-tempered hackers.
Differentiated datasets are a way to collect and combining it with “noise” to create anonymous synthesized data. This interaction takes the real data and creates interactions that are similar to, but ultimately different from, the original input. The goal is to create new data that resembles the input without endangering the owner of the real data.
You can further secure synthetic data through proper security maintenance of corporate records and accounts. Use password protection enabled PDFs can prevent unauthorized users from accessing the private data or sensitive information they contain. In addition, company accounts and databases in the cloud can be secured with two-factor authentication to minimize the risk of improper data access. These steps may be simple, but they are important best practices that can go a long way in protecting all kinds of data.
Put everything together
Synthetic data can be an incredibly useful tool to help data analysts and AI make informed decisions. It can fill in gaps and help predict future outcomes if configured right from the start.
However, it takes a bit of tact not to compromise real personal information. The painful reality is that many companies already ignore many precautions and will eagerly sell private data to third-party vendors, some of which can be compromised by malicious actors.
Therefore, business owners who intend to develop and use synthesized data should set proper boundaries in advance to secure private users’ data to minimize the risks of sensitive data leaking.
Consider the risks involved in synthesizing your data to remain as ethical as possible when considering private user data and maximizing its seemingly limitless potential.
Charlie Fletcher is a freelance writer on technology and business.
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