To remain private in 2022 is no easy task. The United Nations this year launched a special division to focus on the development of PET tech, or Privacy Enhancing Technologies. China being a major player in the UN, naturally it has much to invest in this venture to ensure that its tech-savvy population can stay safe online.

PET is still in its early stages of development, but in an age where everything including your voice, bank details and personal conversations are all stored across the internet, they may prove to be a powerful guardian in years to come.

The balance to extract big data and take advantage of its full socio-economic potential, while at the same time protecting our privacy and security, is a difficult one to strike. Virtual assistants such as Siri or Alexa can recognize their owners' voices by listening to them as much as possible. This means that the more they hear, the greater machine learning processes can foresee and personalize user services. This though comes at a price, as your personal biometric data feeds a system which consumes more and more in order to gain intelligence. Whether this be autonomous cars, healthcare or energy sectors, the growing need for PETs is stronger than ever.

One such PET method is called Homomorphic Encryption. Sensitive data is scrambled into something unintelligible, but still allows for information to be passed around to different platforms and analyzed, while maintaining confidentiality. It is the equivalent of passing a wrapped gift round to different people, allowing each person to make alterations to the gift without opening the box to see what is inside. This method is currently being trialed in finance sectors with great promise, however the process is still limited to operate with a limited volume of data as of course the less each party knows about the data, the less they can analyze it.

Another method is the concept of Federated Learning. This method enables automated learning models such as voice assistants to collect your data but importantly ensures that the information does not leave the device. In this analogy the "gift "does not leave your personal device it was generated on, and allows for full confidentiality without analysis being compromised. Data is no longer routed to a tech company's cloud in order to be processed, and stays safely on your personal device. This technique is still being developed by the likes of Facebook and Google, who are no strangers to data breach concerns in the past and require solutions which allow learning systems to grow without exposing consumers.

Zero proof knowledge is another promising method which excites many in the banking and insurance sectors. This technique involves using cryptographic algorithms through which a tester can mathematically prove to a verifier that a statement is correct without having to actually reveal the data itself. Analogies programmers use to describe this method involve a secret cave in which there are two possible convergent routes for someone to take, but one route is guarded by a security coded door. By demonstrating that the route is taken through the door and appearing at the point where the two routes intersect without showing the code itself validates the data set, without giving away the actual code. Of course there is a 50 percent chance that either route has been taken, but by demonstrating that the person has repeatedly emerged through the coded door route each time quickly reduces this to a mathematical impossibility.

This way of validating statements has many potential uses in the banking or insurance sectors, for example if someone is required to prove their age to access a service.

Our modern economy is based upon data. However, our over-reliance and fragility of this information leaves us vulnerable. By adopting PETs, societies of tomorrow will be able to reap the full rewards of big data, without every aspect of our personal and professional lives being exposed to anyone with an internet connection and malicious intent.

Barry He is a London-based columnist for China Daily.