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submitted 11 months ago by throws_lemy@lemmy.nz to c/physics@mander.xyz
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[-] Gork@lemm.ee 16 points 11 months ago

I've got an unconventional application idea for this particle accelerator on a chip.

True random number generation. There's loads of random information that can be measured from such a device in a controlled manner.

If you could fit one of these on a motherboard then you wouldn't even need to call a pseudo random number generator algorithm anymore, you can pull data directly from the chip.

[-] e0qdk@kbin.social 13 points 11 months ago

There's already hardware RNGs on computer chips -- e.g. the RDRAND instruction on most x86 chips from the last decade or so uses a hardware entropy source as part of its behavior. The quality, of course, is one of those things people go "Uh, can I really trust this...?" about though.

Additionally, PRNGs still have uses even if you do trust hardware RNGs; determinism is a very useful property in software -- it is way, way easier to debug something deterministic (by running a PRNG with a specific seed over and over while testing) even if you want the final version to be randomized unpredictably for users. They also tend to be faster.

[-] Hamartiogonic@sopuli.xyz 2 points 11 months ago

I’ve heard that you could pull random numbers from a basic thermometer. Is a hardware RNG just based on measuring the random noise of some measurement like that?

[-] e0qdk@kbin.social 4 points 11 months ago

This documentation from Intel says of the entropy source that "The ES runs asynchronously on a self-timed circuit and uses thermal noise within the silicon to output a random stream of bits at the rate of 3 GHz." By thermal noise, I believe they mean this sort of noise but this is not my subject of expertise (I'm a programmer, not an EE or physicist). Not sure what AMD uses, but probably something similar, I'd expect.

[-] Cicraft@lemmy.world 7 points 11 months ago

Sounds more complicated than what it's worth tbh

[-] KairuByte@lemmy.dbzer0.com 0 points 11 months ago

You underestimate what a truly random number generator would be worth then.

[-] luckystarr@feddit.de 14 points 11 months ago

There are easier ways to get the same level of randomness.

[-] KairuByte@lemmy.dbzer0.com 0 points 11 months ago

The same level as locally truly random? What provides that same level of random?

[-] Nomad@infosec.pub 7 points 11 months ago

Proovably secure PRNGs are as secure as TRNGs. All you need is enough entropy and that you can get from plenty of sources.

A single chip you rely on for entropy is a problem as you cant look inside. Therefore you cant trust it fully.

[-] KairuByte@lemmy.dbzer0.com 1 points 11 months ago

While they may be as secure, I would not call that the same level of random. I’ll agree they are equal in almost every use case, but truly random is still “more random” in comparison.

Though I’ll concede that if it can’t be proven to be truly random, it’s not of much use.

[-] FooBarrington@lemmy.world 3 points 11 months ago

How do you measure the amount of "true randomness"? CSPRNGs can use very little entropy to generate large amounts of random data. Mathematically speaking there isn't any difference between that and what you call "true randomness" - if there was, they wouldn't be CSPRNGs.

[-] KairuByte@lemmy.dbzer0.com 1 points 11 months ago

Truly random would be something that is impossible to reproduce. While you are correct that we can approximate randomness, the final calculation can always be replicated if the initial inputs are known. Just because something is exceedingly difficult to replicate, doesn’t mean it is truly random.

Think of it like cleaning your pool. You have a vacuum, chemicals, the system circulates, maybe a skimmer or a net. You can get the pool to the point that it is acceptable to swim in, but you’re never actually swimming in a clean pool. In a similar manner, current random number generators get you to a point that you are (usually) fine assuming the number is random, but it never really is.

[-] FooBarrington@lemmy.world 3 points 11 months ago

I know what you're trying to get at, but my point is this: Imagine you have two streams of data, one from a CSPRNG, and one from what you call "true randomness". How can you tell which one is which (as long as you're staying under the CSPRNGs limit from your initial entropy)?

If you can't tell me a way, there is no functional difference between these two options. So what advantage would true randomness hold?

[-] KairuByte@lemmy.dbzer0.com 0 points 11 months ago

I said this in another comment, but while I agree that there is virtually no functional difference, and in the vast majority of cases truly random and functionally random are equivalent, that doesn’t mean that something which is functionally random is truly random.

[-] FooBarrington@lemmy.world 3 points 11 months ago

But it is truly random for all intents and purposes, since the input is truly random. Just because the process contains deterministic steps doesn't mean the input entropy isn't true entropy anymore.

[-] KairuByte@lemmy.dbzer0.com 0 points 11 months ago

And a pool is clean for all intents and purposes. There is still a distinction though. The fact that it is deterministic inherently makes it less random than true randomness.

[-] FooBarrington@lemmy.world 3 points 11 months ago

The input is not deterministic.

[-] KairuByte@lemmy.dbzer0.com 0 points 11 months ago

If you take the original values used to determine the final “random number” and run them through the same sequence of calculations, you will always reach the same value.

We rely on the fact that the inputs are so numerous and/or difficult to replicate to deem the final value “random”. But that doesn’t mean that the value cannot be reached by a second party given perfect knowledge of the original state of all inputs.

True randomness, on the other hand, is impossible to calculate even with that perfect knowledge, because we aren’t relying on the state of inputs running through a calculation.

[-] FooBarrington@lemmy.world 3 points 11 months ago* (last edited 11 months ago)

But that's my point: just because you apply deterministic steps to a truly random input doesn't make the output not truly random. You use real entropy as your starting point, which is literally exactly what you call "true randomness". This means the output has the same level of "true randomness" as your "truly random" input, because you mathematically don't lose entropy along the way.

To put it more simply: you're arguing from a philosophical perspective, not a mathematical one.

[-] KairuByte@lemmy.dbzer0.com 0 points 11 months ago

The input is not truly random though. If it was, we could just use that input, with no other steps, and have a truly random output. You’re confusing an unknown state with randomness.

[-] FooBarrington@lemmy.world 2 points 11 months ago

No, it actually and literally is truly random. You'd need to know everything about the hardware itself and the environment around it in incredible detail (incl. the temperature of every individual small patch of material, air flow and the state of air in and around the case) to reliably predict the initial entropy for a given modern system, since tiny changes in e.g. temperature will completely change the input.

It's only a small bit of entropy, but enough to kick-start the RNG in a way that can reliably create high-quality entropy.

[-] KairuByte@lemmy.dbzer0.com 0 points 11 months ago

So you’re literally arguing that knowable inputs, however unlikely knowing those inputs might be, run through known deterministic calculations, results in a guaranteed unknowable output?

[-] FooBarrington@lemmy.world 2 points 11 months ago

No, I'm arguing that the inputs aren't knowable to the required degree in the general case, which defines their entropy, and that entropy isn't mathematically lost, it's improved through deterministic calculations.

[-] KairuByte@lemmy.dbzer0.com 0 points 11 months ago

The same was thought about previous iterations on random number generators. The first I am aware of used an extremely precise time stamp, and ran the calculations on that. On the assumption that no one could possibly know the exact timestamp used. That was obviously untrue, which can be verified by the fact that such systems have been broken before.

Just because you can’t conceive of a way to know the values, does not make them unknowable. It just makes it improbable to happen.

And again, I’m not saying the random numbers we can produce now are currently breakable. But that doesn’t mean that a decade from now, or even a century, they will remain unbroken.

[-] FooBarrington@lemmy.world 2 points 11 months ago

Say I'm restarting my phone, and it uses details like temperature fluctuations in CPU sensors as entropy. How would you know all the required values? Since I'm holding the phone in my hand, the temperature of my hand (and consequently body temperature) are relevant, not to mention the air around my phone. How would you find those values at the exact time the sensors are read?

[-] KairuByte@lemmy.dbzer0.com 1 points 10 months ago

You honestly think those values aren’t possible to estimate within a range then brute force?

That’s like asking “say I hit a button at a very specific time, how would you find that exact time?”

[-] FooBarrington@lemmy.world 1 points 10 months ago

Yes, because 1) you'd need to know them with incredible precision, and 2) you can't brute force, because you only have one chance. Otherwise you can also brute force anything that's "truly random" as you put it.

That’s like asking “say I hit a button at a very specific time, how would you find that exact time?”

That's the thing, it's not like that. It's more like "say I hit a button at a very specific time and roll hundreds of dice, how would I find that exact time and all the results of those dice".

[-] KairuByte@lemmy.dbzer0.com 1 points 10 months ago

Apart from the face that there are absolutely no “dice rolls” involved. They are known deterministic calculations. Because in order to add “dice rolls” you would need randomness. You can’t have a non deterministic calculation involved, because that isn’t how computers work.

You’re essentially saying “take a knowable input, add true randomness, output true randomness using nothing but knowable inputs!”

And you absolutely can brute force it. Why would you have a single chance? Because of arbitrary rules?

As for true randomness, you’re getting a range of “extreme low to extreme high” which isn’t currently brute forcible.

[-] FooBarrington@lemmy.world 1 points 10 months ago* (last edited 10 months ago)

My dude, the "dice rolls" are all the small pieces of entropy that you'd have to know the environment to an unknowable degree for. They are conceptually absolutely involved, and if you can't understand that, we're done here, because you're handwaving over the difficult parts in a way that doesn't make sense.

[-] KairuByte@lemmy.dbzer0.com 1 points 10 months ago

Those “dice rolls” are not random though. The problem is you keep talking about these inputs as if they themselves are random. They aren’t. And just because you can’t fathom a way to know their values now, doesn’t mean they are unknowable. I point back to the timestamp issue, where at the time it was considered “enough” but was disproven as such years down the line.

[-] Nomad@infosec.pub 1 points 11 months ago
[-] hansl@lemmy.world 1 points 11 months ago

Honestly you won’t be able to build a device with this thing in it for cheaper than alternatives. For home usage it’s about 50-100$. And a good enough PCI card like Quantis will be 3000$ with a bandwidth of 240Mbps.

And that’s not even discussing bandwidth. In most cases bandwidth (number of random bits generated per second) is the limiting factor in usage. You want them to be fast enough that when you need a number you’re not waiting for it.

[-] John_Hasler@lemmy.one 1 points 10 months ago

Yes. It isn't hard to generate random numbers in hardware. It is hard to generate them very fast. This device would not help solve that problem.

[-] stebo02@sopuli.xyz 1 points 11 months ago

You can already make/buy a Quantum RNG for truly random numbers.

this post was submitted on 26 Oct 2023
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