HN842: How Network Engineers Can Prepare for a Post-Quantum World

Drew Connery-Murray (0:00)

Welcome to Heavy Networking. If you hear the song ABC by The Jackson 5, and you mentally substitute BGP in your head, you've found the right podcast. I'm Drew Connery-Murray, Ethan Banks is away, so Scott Robohn is joining as guest co-host. You can hear Scott on the Total Network Operations podcast, right here on The Packet Pushers Podcast Network. On today's show, we're going to dig into post-quantum readiness. Now, you may have heard of Q-Day. Q-Day signals the emergence of a cryptographically relevant quantum computer that can break public key encryption.

Q-Day itself is actually unknown the date, but the tech industry is making preparations now. Network and security vendors, the big browser providers, cloud companies, they're all rolling out support for post-quantum algorithms. So on today's show, we're going to dig into post-quantum cryptography or PQC. We'll talk about what network engineers need to know to test and integrate post-quantum algorithms into their infrastructure, why organizations might want to think more broadly about things like crypto agility and more.

Our guest is Bill Dockery. He is Consulting Solutions Architect at WWT. He's been thinking, writing and talking about post-quantum cryptography for network engineers. He's here to share what he's learned. We're going to spend the first part of the podcast on level setting and then move into what's relevant specifically for network engineers. And then we'll wrap up with some big idea kind of questions.

SPEAKER_2 (1:12)

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Drew Connery-Murray (1:39)

So Bill, welcome to the podcast. Just to kick us off, how does quantum computing put classical cryptography at risk? And what do those risks mean for organizations?

Bill Dockery (1:48)

Well, thank you for having me here and answer those kind of questions. So quantum computing is going to basically crack what we call classical cryptography. It says RSA is at risk. And so with that, you can basically take a encrypted communication that you wouldn't typically use today and determine what the private key was. And in doing so, we work through the entire conversation itself and pull all the data out from between it. And what it takes there is it's a different mindset. The reason why cryptography today, classical cryptography, we're going to be calling that classical from here on out.

The reason why it's so viable is that data has a life cycle. And for RSA with traditional computers, the way they operate, the way they act, the way the operating systems are built, and the way GPUs work in general, it took a lot of time. It really was a time thing, right? It could be as long as 100 years, a thousand years before someone could crack the encryption on a particular communication. And that all changes because quantum computers are very different in how they work. And because of that, they could do a little bit of different math. And the one math is a little more powerful than the other one. I can take this now to classical cryptography could be cracked. And they're saying minutes, right?

Within a very reasonable amount of time for a hacker who wanted to do something malicious, very reasonable amount of time for them to do that stuff. And so now when you consider the length of data viability, the life cycle of the data itself, that's not good.

Drew Connery-Murray (3:22)

So can you explain at a high level why quantum safe algorithms aren't susceptible to attack by quantum computers?

Bill Dockery (3:28)

Yeah, it comes back to the math. Honestly, the placebook cryptography up until now used prime numbers as the basis, the foundation for how the math became hard for GPU to crack. And it just would take, again, the length of time, they didn't have an algorithm, they just do that algorithm would have taken forever. And the new PQ, like, let's say ML chem, the math in there, when you look at RSA, which is the classical side of things, it was basically multiplying prime numbers together, and it was really hard to factor backwards. And the new one, ML chem, the best way I could describe it, to make it layman's kind of terms, the way it was explained to me, because I am a layman, is that the ML chem, it actually introduces noise, right? So it's not the same foundation, right?

It introduces a bit of noise that can be taken out on the far end, right? So you can actually have the conversation with another host. And that complete different process is difficult for a quantum computer. Because I think maybe one good misconception that people have is that quantum computers is not just the computer you have on steroids, right? There are things that GPUs are really good at, the kind of math that they do, right? And that's why we came out with GPUs and all sorts of other co-processing.