This document discusses random numbers and their importance for cryptography. It begins by asking what random means for computers and how they can generate random values. Random numbers are needed for encryption algorithms to produce unpredictable encryption keys. True randomness comes from atmospheric or radioactive sources, while pseudo-random numbers from software can be predicted. The document also briefly mentions attacks that can be done on random number generators if they are not truly random.

1. Cryptography -
Random Numbers
5 minute series
Abdul Manaf Vellakodath
Security Architect

2. Random - What does it mean?
How does - computers - the
devices which could only
provide a completely
predictable outcome; could
generate random values?
Are those random numbers
that we generate for
cryptography are really
random? If so, how random
is it?

3. Random Numbers - Why do we need it?
Encryption Process
Data
Encryption
Algorithm
e8)iuk2323*0232323>2324
Jlsdojfakjdpot
23kj[8JSsdfhwlasduo7wtq owjdl
qjniluagSOCIay
sdo 87peuqiwjeb qy8wepqwnd
asiuyc98h2eh13kl
2ey9yhdjady089ypnl oq34ykllasd
aoe8dyihq3 oq24yplkand;aify098y
po123 q3yeq983748edklwhk
hsadhaoisyd8yr3qpohd
Encryption has typically three elements:
1. Data (message to be protected)
2. Encryption Algorithm (This is public
information - mostly)
3. Encryption Key (Secret, except for the Public
Key in the Public Key algorithm.)
The only element that protects the secrecy of data
is the secrecy of the ‘encryption key’ used; as the
algorithm is already public.
If you can predict or guess the ‘encryption key’, you
can break the encryption to get the data that is
protected.
Some examples of random keys:
1. Session Key in a Web Browser
2. Encryption Keys in RSA/AES256 algorithms
3. Lotteries and draws

4. http://lxr.free-electrons.com/source/drivers/char/random.c#L66; Copyright Matt Mackall <mpm@selenic.com>
Random Numbers - In words of a cryptographer!

5. Random Numbers - Random Distribution
Suppose,
we plot randomly generated values in x-y coordinates! Let’s say (x= some measurement, y=probability)
More
predictable
More
Random

6. https://blog.cloudflare.com/why-randomness-matters/
A Simple Pseudo-Randomness - An illustration!
The middle
square
method is
trivial to
predict: the
next number it
generates is
entirely
determined by
the number it
last produced

7. True Randomness
A random number generated from the following, or a combination of the same are
considered to be truly random:
● Atmospheric noise levels
● Variation in radioactive decay timings
● A variety of random bits from larger computer networks

8. Attacks on Random Numbers
https://en.wikipedia.org/wiki/Random_number_generator_attack