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The tangible world we were born into is steadily becoming more homogenized with the digital world we’ve created. Gone are the days when your most sensitive information, like your Social Security number or bank account details, were merely locked in a safe in your bedroom closet. Now, private data can become vulnerable if not properly cared for.
This is the issue we face today in the landscape populated by career hackers whose full-time jobs are picking into your data streams and stealing your identity, money or proprietary information.
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Although digitization has helped us make great strides, it also presents new issues related to privacy and security, even for data that isn’t wholly “real.”
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In fact, the advent of synthetic data to inform AI processes and streamline workflows has been a huge leap in many verticals. But synthetic data, much like real data, isn’t as generalized as you might think.
What is synthetic data, and why is it useful?
Synthetic data is, as it sounds, made of information produced by patterns of real data. It’s a statistical prediction from real data that can be generated en masse. Its primary application is to inform AI technologies so they can perform their functions more efficiently.
Like any pattern, AI can discern real happenings and generate data based on historical data. The Fibonacci sequence is a classic mathematical pattern where each number in the sequence adds the prior two numbers in the sequence together to derive the next number. For example, if I give you the sequence “1,1,2,3,5,8” a trained algorithm could intuit the next numbers in the sequence based on parameters that I’ve set.
This is effectively a simplified and abstract example of synthetic data. If the parameter is that each following number must equal the sum of the previous two numbers, then the algorithm should render “13, 21, 34” and so on. The last phrase of numbers is the synthetic data inferred by the AI.
Businesses can collect limited but potent data about their audience and customers and establish their own parameters to build synthetic data. That data can inform any AI-driven business activities, such as improving sales technology and boosting satisfaction with product feature demands. It can even help engineers anticipate future flaws with machinery or programs.
There are countless applications for synthetic data, and it can often be more useful than the real data it originated from.
If it’s fake data, it must be safe, right?
Not quite. As cleverly 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 doorway hackers need to find, manipulate and collect the personal information of user samples.
This is where the issue of securing synthetic data comes into play, particularly for data stored in the cloud.
There are many risks associated with cloud computing, all of which can pose a threat to the data that originates a synthesized data set. If an API is tampered with or human error causes data to be lost, all sensitive information that originated from the synthesized data can be stolen or abused by a bad actor. Protecting your storage systems is paramount to preserve not only proprietary data and systems, but also personal data contained therein.
The important observation to note is that even practical methods of anonymizing data don’t guarantee a user’s privacy. There is always the possibility of a loophole or some unforeseen hole where hackers can gain access to that information.
Practical steps to improve synthetic data privacy
Many data sources that companies use may contain identifying personal data that could compromise the users’ privacy. That’s why data users should implement structures to remove personal data from their data sets, as this will reduce the risk of exposing sensitive data to ill-tempered hackers.
Differentiated data sets are a mode of collecting users’ real data and meshing it with “noise” to create anonymous synthesized data. This interaction assumes 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 compromising the possessor of the real data.
You can further secure synthetic data through proper security maintenance of company documents and accounts. Utilizing password protection on PDFs can prevent unauthorized users from accessing the private data or sensitive information they contain. Additionally, company accounts and cloud data banks can be secured with two-factor authentication to minimize the risk of data being improperly accessed. These steps may be simple, but they’re important best practices that can go a long way in protecting all kinds of data.
Putting it all together
Synthetic data can be an incredibly useful tool in helping data analysts and AI arrive at informed decisions. It can fill in gaps and help predict future outcomes if properly configured from the onset.
It does, however, require a bit of tact so as to not compromise real personal data. The painful reality is that many companies already disregard many precautionary measures and will eagerly sell private data to third-party vendors, some of which could be compromised by malicious actors.
That’s why business owners that plan to develop and utilize synthesized data should set up the proper boundaries to secure private user data ahead of time to minimize the risks of sensitive data leakages.
Consider the risks involved when synthesizing your data to remain as ethical as possible when factoring in private user data and maximize its seemingly limitless potential.
Charlie Fletcher is a freelance writer covering tech and business.
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