
In a world where data security is an ever-growing concern, Joshua Tyler—a computational engineering doctoral candidate and electrical engineering research associate at UTC—has broken new ground.
Tyler, who is on track to receive his third UTC degree in May, has developed the world’s first usable Artificial Intelligence network that can learn how to encrypt itself. This AI network, he said, can provide nearly unbreakable cryptography—significantly improving the security of communications.
Tyler and his faculty mentor, Dr. Don Reising, a Guerry and UC Foundation associate professor of electrical engineering, have uploaded a draft of their publication to arXiv—an open-access repository for scholarly papers. They have already submitted their invention disclosure to the University of Tennessee Research Foundation for a provisional patent.
Tyler’s AI network, he explained, learns to encrypt data “by transforming an encryption key onto the original unencrypted data. The goal is to ensure that the encrypted message is unique to the key while the original message is still recoverable on the other end.”
“This ensures that when deployed, each encryption is unique and significantly extends the network’s lifespan,” said Tyler, who received a bachelor’s degree in electrical engineering in 2020 and a master’s degree in 2022.
The research builds on a concept initially proposed by Google, known as Adversarial Neural Cryptography. While Google demonstrated the potential for AI-driven cryptography, its approach faced significant limitations—particularly in ensuring the encryption key’s influence on the encrypted message and additional communication overhead.
“I copied over Google’s setup and trained their network on my side,” he said. “The network was encrypting the information, but we found out that there wasn’t a lot of uniqueness on the encrypted side when we were switching keys, so that makes the overall life of the network shorter. You’d only get to encrypt one message per network.”
Reising, who has worked with Tyler for more than six years, said he “basically challenged Josh to go and find a way to get this thing to generate a unique code or a unique encoded message.”
“And that’s what he did,” Reising said. “He went off and worked on developing his own technique.”
Reising recalled a pivotal moment during the process.
“I asked him, ‘What architecture are you using? Are you using CNN? Are you using an LSTM? What are you using?’
“And he’s like, ‘No, I’m not using any of those. I made my own.’
“I said, ‘What do you mean you made your own?’
“He said, ‘I made my own and it’s a deep learning network.’ That was crazy and it was pretty awesome.”
By rethinking the structure of AI networks, Tyler developed a “novel neural network architecture” that addresses these challenges.
The result is a network that offers nearly unbreakable encryption and unparalleled adaptability in safeguarding sensitive data.
“I changed the network architecture so that the influence of the key was still maintained through the entire structure of the network,” he said.
A crucial feature of Tyler’s system is its rapid adaptability, which allows it to retrain itself in seconds to produce entirely new cryptographic algorithms. This new architecture ensures that each encryption remains unique, effectively overcoming the limitations of previous methods.
“Every time you retrain the network, you get a different cryptographic algorithm,” Tyler said. “So then, even if you use the same key across two differently trained networks, you’ll get a new encryption scheme.”
“These things train really fast so that we can have a new cryptographic algorithm in about 16 seconds.”
Tyler said it took several months of trying to get it to work with the traditional architectures, “just trying different training scenarios and different metrics that the network would use to update itself.”
“When it finally worked,” he said, “it was surprising because you go through two or three months of it not working—and then you try something completely new that you invented and it suddenly works. That’s where the real fun starts because then you get to make sure that it really works.”
Tyler admitted he felt a “slight bit of disbelief” when he realized his discovery was—indeed—a discovery. “When you get it to work that first time, you’re like, ‘Was that a lucky guess?’ I think it was a little bit lucky—but it was also hard work to map it out mentally, write the code to run the algorithms, and then see how well it holds up against what’s already out there.”
The implications of Tyler’s discovery extend across industries—including defense, health care and finance.
“In the military, they want to do a lot of unmanned vehicles, and a really big consideration is communications,” he said. New aircraft are being designed to be unmanned because it costs a lot of money to put a human pilot in the cockpit.
“You have to have life support systems, you have to have G-force and you have to pay to train that pilot—and that takes millions of dollars,” he continued. “If you have an unmanned vehicle, you can go faster and you don’t risk the pilot’s life every time they go out on a mission or do training—but that requires coordination between the operating base and the unmanned vehicle. It has to communicate a lot of information, but we don’t want that information to be gleaned by any adversary.”
He described a real-world incident in which an unmanned vehicle operated by a drone pilot lacked encrypted communications. An adversary intercepted the data, resulting in the loss of the aircraft.
“They reverse-engineered it,” he said, “so we lost that capability because we weren’t encrypting our communications to the drone.”
In other areas, such as health care, the technology could secure sensitive information transmitted between devices.
“In a hospital, you have the vitals being read in the patient’s room—and that’s being fed to the nurse’s station to where they can monitor that,” Tyler said. “There may be someone that wants to intercept that information for some reason. So, we would prevent them from being able to intercept a patient’s vitals.”
With a draft already published on arXiv, Tyler and Reising are preparing to submit the work for peer-reviewed publication.
“Part of it is we submit the paper and hopefully get that published,” Reising said, “and look to see what the application space is. Who’s interested in it? Is it something that people would license?”
As Tyler’s mentor, Reising has taken great pride in watching his protégé’s career growth. Mentors, he said, are meant to guide.
“My job is not to impede, not to hinder and not to stymie. It’s to move obstacles and enable,” he said. “When something like this happens, it tells me two things: I’m getting to work with somebody really special who really can do some really great things—and I’m doing something right in terms of the mentorship.
“I’m enabling the student to achieve those things and put them in a place so that they can follow their passion, solve problems and push the paradigm of what we know and understand—which is really cool, really exciting and rewarding as a professor.”