Data is one of the most powerful assets nowadays. Besides being an analytic tool, it’s tradable, and buyable, as companies and institutions all around the globe seek to be the best at what they do, and information on what, how, and when people act is key to being number one. Now how can data be used to save energy, or even create synergies in the medical research field without ever having to experiment with animals or people? Digital twins might be — one — answer to it. By digitally replicating something with high accuracy, one can easily foresee consequences and obstacles, but also ways to mitigate them, and how to make the most of them.
Digital twins are virtual models of things from the real world, which can be people, objects, and even entire cities. They are used to better understand the consequences and effects of certain actions in real life and can be used for virtually anything, from predicting the success rate of a new drug based on a patient’s medical history to enhancing a powerplant’s production by digitally mimicking it.
This was strongly debated in this year’s World Economic Forum (WEF), and there’s even a research project regarding twin cities. According to such a project, “the digital twin city offers a model of urban planning and construction for future sustainable development that effectively combines innovations in digital technology with urban operational mechanisms, and provides a feasible path for urban upgrading. Through the precise mapping, virtual-real integration, and intelligent feedback of physical and digital cities, it promotes safer, more efficient urban activities and more convenient and inclusive everyday services, as well as helps to create more low-carbon, sustainable environments”.
Some clinical trial enterprises are already making use of digital twins: they replicate the participants’ medical details — DNA, health records — into digital form to study the effects of a certain substance. According to an article by Urtė Fultinavičiūtė, “a digital twin can offer several benefits to trial participants. First, it allows researchers to personalize treatments and gain insight on how to treat the condition better”.
The logic can be replicated in countless other industries, with endless use cases. All this sounds incredibly positive and useful, but it’s easy to see some technical, ethical, and even security issues, namely when it comes to sensitive data breaches. There’s a debate going on regarding privacy when it comes to digital twins. Developers and engineers need to retrieve real-world data to be able to build a truthful system, and that’s not possible without gathering personal information.
In theory, Confidential Computing could be used to tackle some of these problems in the sense that it allows you to retrieve relevant and useful data without ever disclosing sensitive raw data, such as people’s identities. If we go back to the new drug case again: the pharmaceutical company could retrieve all the relevant data from the patient’s medical records without ever uncovering details that are unnecessary for its research, such as his name, address, etc.
Blockchain technology aims to be public by default, but some projects — Integritee included — are paving the way to foster privacy without compromising transparency. This means the technology’s architecture remains the same but is empowered with privacy-based features. Why? Because no one wants to have everything out in the open all the time. Privacy is needed in countless areas, especially when it comes to personal information and confidential data that can’t be disclosed.
What if you could build your own digital twin application with a privacy-based blockchain project? In theory, this opens a whole new world for this simulation trend, as it eliminates big issues like cybersecurity threats, privacy concerns, lack of scalability, and integration. Integritee powers you with a scalable network and a ready-to-use SDK to build use case-specific L2 Sidechain applications. If you want to know more about building with Integritee, check our website or documentation.
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