• Infinite Noise TRNG - True Random Number Generator

The Infinite Noise TRNG is an affordable and secure true random number generator (TRNG). It’s based on a modular entropy multiplier technique that continuously loops over previous random output, gathering randomness from the noise of the hardware components along the way, to generate the next random output. This way it produces a constant, measurable level of entropy which is then whitened to produce true random numbers. The openness of the implementation makes it easy to inspect and verify, as all security hardware should be!

Who Needs It and Why?
 
Almost any cryptographic operation relies on random numbers - and there are many different approaches to collect this entropy. For Linux systems, this is mostly done with user input and other hardware timing’s “randomness”. This approach is very slow and its performance mainly depends on the computer’s activity.For situations where the Linux Random Pool /dev/random depletes and finally blocks, hardware random number generators like the Infinite Noise TRNG provide a secure, high performance way to feed entropy into your computer.
  • When you operate many virtual machines, lack of entropy can be a serious problem as all VMs share the host’s entropy source. The Infinite Noise will provide lots of random numbers to the host system.
  • Large web-servers doing SSL encryption can be sped up by using hardware random number generators, while making communications more secure.
  • If you operate Linux-based WiFi access points (whether using a PC, laptop, or custom hardware), they can benefit from plentiful available entropy, as WPA2 authentication relies on it.

The next big thing about random number generators is security. When you take a close look at the last 20 years of information technology, there have been dozens of security incidents with flaws in random number generators.

  • 1994: First Netscape SSL implementations using a predictable seed.
  • 2007: Reverse engineering showed that the Windows 2000/XP random number generator had a bug that makes it vulnerable. It’s been there for years.
  • 2008: Members of the CCC developed an attack overcoming a poor RNG used in MIFARE Crypto-1, which is still used in some RFID tags.
  • 2008: The random number generator used in OpenSSL had a critical bug - possibly affecting the security of all keys generated before 2008.
  • 2013: Snowden documents unveiled that the Dual_EC_DRBG algorithm has been backdoored by the NSA as part of the Bullrun project. Even worse - it has been recommended by the NIST and actually adopted by manufacturers. At the same time discussions concerning the use of Intel’s RdRand (previously named: Bull Mountain [1]) in the Linux kernel came up. Doesn’t that name sound alarming enough? But even worse - there seem to be ways to let it produce not-so-random numbers. [2]
  • 2016: The Linux driver for a very common wireless card (ath9k) appears to have a HWRNG which was not designed to be used as security device. Its just an ADC - and nobody knows where the data comes from. But one thing is for sure: It’s not very random and dominated the Linux entropy pool - which is really bad. Thats why kernel developers decided to disable it by default - but the change did not arrive in all distros yet.
In all these scenarios, the Infinite Noise TRNG helps you to generate random numbers with confidence. A good entropy source can’t help you with everything - but it’s fundamental for all secure applications.Most of the points in this list of RNG related flaws can be found in the Prominent examples of the Wikipedia article “Random number generator attacks”. For the rest, I’ve added some references.
 
Use Cases:
 
The Infinite Noise TRNG is useful in any situation requiring more entropy than normally available on a typical personal computer. The Infinite Noise TRNG is a good choice if you are:
  • Building your own certificate authority
  • Administering wireless access points with WPA2
  • Running a high-traffic server with SSL enabled
  • Having fun with one-time pads
  • Replacing /dev/urandom with a more secure and faster /dev/random
  • In need of a strong password

How it works:

The hardware derives entropy from thermal noise, like many random number generators. What divides it from other TRNGs is the modular entropy multiplication technique.Thermal noise of resistors is being amplified in an infinite loop to generate data – which is not totally random yet. By using modular entropy multiplication there is some correlation of adjacent bits in the stream.Health monitoring of important parameters of the raw datastream is implemented in the devices driver, which then uses the SHA-3 hashing function for cryptographic whitening to produce true random numbers.

There is no way to override the signal without being noticed by the driver. Of course it’s possible to influence it a bit, but because we use modular entropy multiplication, this only makes the output slightly more random.Since by definition there are no patterns in random data, how can you know the data coming from your entropy source was not spoofed? The Infinite Noise TRNG produces this predictable level of entropy, just because it’s the only way to constantly verify the hardware is working properly. And only then will it apply whitening with the SHA3 hashing function.It may sound daunting, but this is the key feature of this implementation, as the foreseeable (and very high) level of entropy enables the driver to monitor proper function of the device. This is an essential feature for any trustworthy random number generator, which most devices unfortunately are missing. Even when you can access the raw output - during operation you often don’t find a way to monitor its operation.
 
Hardware:
 
The circuit implementinh modular entropy multiplication consists of a pair of a charge capacitors (C8, C9), opamps, comparators COMP1 andCOMP2, solid state switches SW1 and SW2. The driver controls these switches to run two modular entropy multipliers in parallel.In the first half of a cycle, we need to determine if the current voltage stored in the capacitor is lower than Vref (2.5V). If thats the case, the whole charge is being multiplied by 1.84. When it is higher, we first need to subtract Vref from it – to prevent overshooting.This logic can be solved very efficiently using the modulo operation.
 
In the signal loop, additional noise is collected from components along the way, which forms our random output.Data is being sampled by readings from the two comparator outputs (tied to the capacitors) resulting in a digital stream that is returned via USB.
 

digital, analog and clock signal of a single entropy multiplier

  • Channel# Connected to Signal       Multiplier#
  • 1 COMP1      Digital                              1
  • 3 C8            Analog                              1
  • 4 SWEN1   Clock signal                       1
 
Features & Specifications:
  • Random output: Default 30 KB/second of random data
  • Interface: USB2.0
  • Supported platforms: (Windows, Linux, and also ARM-support)
  • Low Power: 8 mA
  • Weight: 10 g
  • Dimensions: 15 mm x 50 mm x 8 mm
  • No firmware
  • Uses off-the-shelf components
  • SHA3-“Whitening” and health monitor built into host drivers
  • Fully open source (see GitHub repo) and OSHWA-certified (#DE000006)

Custom Security Seals (5K)Stretch Goals

When the campaign surpasses the $5000 goal, every device (with enclosure) will be sealed with security seals with its serial number imprinted on them, thus making the device tamper-evident in terms of FIPS. In my opinion, it’s already pretty evident without them, as you can see in this image of one of my prototypes which needed a repair once.

 

In any case, by using modular entropy multiplication, the Infinite Noise TRNG is very robust against hardware attacks, as the driver will detect changes in the behaviour of the device.Thus, the best way to verify its correct operation is to use our signed driver packages - or compile it yourself after you’ve checked the code integrity - and use the --debug parameter to take a look at the raw data. Or just run the testsuite available on GitHub (manual interpretation required!)

 

Package Contains:

1 x Infinite Noise TRNG

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Infinite Noise TRNG - True Random Number Generator

  • Brand: Crowd Supply
  • Product Code:CS-Infinite-Noise-TRNG
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