Welcome to Cryptography and Information Theory! This course combines cryptography (the techniques for protecting information from unauthorized access) and information theory (the study of information coding and transfer). More specifically, the course studies cryptography from the information-theoretical perspectives and discuss the concepts such as entropy and the attacker knowledge capabilities, e.g., Kerckhoff's Principle. It also contrasts information-theoretic security and computational security to highlight the different train of thoughts that drive the cryptographic algorithmic construction and the security analyses. This course is a part of the Applied Cryptography specialization.
Entropy: General Distribution Case
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來自 University of Colorado System 的課程
Cryptography and Information Theory
60 個評分
從本節課中
Information Entropy
This module studies information entropy to quantify randomness. Information entropy can be used to measure the strength of the key or the secret information that determines the security of a cryptosystem against an attacker who does not know the random key.
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Sang-Yoon ChangAssistant Professor
Computer Science
Previously, we looked at
information entropy when the probability is uniformly distributed.
So this was the Hartley's construction in 1928,
Hartley's formula in 1928 for uniform distribution case.
Again, that's where the uniform distribution comes from.
It comes from the modeling of the outcome distribution.
Now, what happens when this is not true when properly distribution is not uniform?
So, let's investigate that.
Let's look into that.
And we will look at a more generalized equation
to express information entropy.
Let's actually build on
what we have on
this sheet and we're going to actually build on this example of weather in Gotham City.
We will be using the same weather information as the random phenomenon,
but let's use a different distribution.
Let's actually use a different city altogether.
I'm just going to get rid of some of the parts that are less relevant.
And this was the uniform case.
And we'll be using the same variable definitions
with small n being the number of independent events.
Instead of Gotham City,
which has a very random weather,
let's use Colorado Springs.
And Colorado is known to be sunny.
So there will be a bias towards the sunny weather.
Again, let's suppose in Colorado Springs,
the weather is either sunny,
rainy, snowy or cloudy.
Let's make that probability distribution non-uniform.
And again, as I mentioned,
Colorado itself is famous for being a sunny state, a sunny Colorado.
So let's suppose that the probability of Colorado Springs being sunny is
half and the probability
of the weather being rainy is 0.125 or 1/8,
the probability of the weather being snowy is also 1/8 or 0.125,
the probability of being cloudy is 0.25 or a quarter.
And these four probabilities will sum up to one.
Similarly to what we did before,
we'll ask the question about how many bits are needed to
communicate the weather in Colorado Springs.
So it's no longer Gotham City,
but it will be Colorado Springs.
And we would want to make
this communication or the bit transfer as efficient as possible.
We can keep this way of encoding,
however, such encoding scheme is not going to be efficient.
Let's actually choose another encoding scheme which is more efficient.
And the way that we're going to do that is we're going to have
the more probable outcomes take smaller number of bits.
So if the bit is zero,
then the weather in Colorado Springs will be sunny.
And let's use a blue pen for that.
And if the first bit is one,
then the weather in Colorado Springs will not be sunny.
So, meaning that it will be rainy or snowy or cloudy.
Now, if that first bit is zero,
then we, as an outsider,
knows that the weather in Colorado Springs is sunny,
but if the bit is one,
then we don't know for certainty
which of the three weather conditions the Colorado Springs is experiencing.
So if the first bit is one,
then we need another bit.
And suppose, if that second bit is zero,
let's suppose that the weather condition of Colorado Springs is cloudy,
and if the bit is one,
then the weather condition is rainy or snowy.
And we're not done if the bit sequence is 1 1.
So, if the third bit after the two bits of 1 1s,
if the third bit is zero,
then the weather condition in Colorado Springs is rainy,
and if the third bit is also one,
then the weather condition in Colorado Springs is snowy.
In this case, we actually have a different number of bits for different weather outcomes.
The sunny weather has only one bit,
bit zero, the cloudy weather is encoded using one zero.
So, the weather being cloudy is encoded with two bits, and likewise,
for rainy, it corresponds to 1 1 0,
and for snowy, also 1 1 1.
Now, we actually see
a different number of bits for different outcomes, more specifically,
one bit for the sunny case and the sunny is most probable,
two bits for cloudy,
and three bits for rainy and snowy.
Let's look at the information entropy.
For one day or one weather event,
so for one day,
we see that the information entropy is equal to
one bit for the case where the weather is sunny,
plus two bits for the case where the weather is cloudy.
And three bits for the case where the probability the weather being rainy,
and also three bits for the probability of the weather being snowy.
And this is the expected number of
bits that you would use for communication.
Notice that this first part is
log of one over P_sub_i of base two and the second part is P_sub_i.
Again, the first part is log of one
over P_sub_i and the second part is the probabilities.
To recap that, we can use Riemann sum to express that.
When this boils down to i equals one to capital N. Again,
small i is the outcome indices,
P_sub_i times log of one over P_sub_i.
And given that this is P_sub_i to the minus first power inside the logarithm,
this is equal to
minus i_sub_one to N P_sub_i
times log of P_sub_i.
So this is still information entropy for one day.
And given small m independent weather events or m independent days,
we actually have the entropy for m days, independent days.
We have the information entropy to be m times of this expression.
So m times i equals
one to N of the Riemann sum P_sub_i log of one over P_sub_i.
And this equation was established by Claude Shannon in 1948,
and it's a generalization and can be used even in
the cases where the probability distribution is not uniform.
Again, to recap that,
H is equal to m times the Riemann sum over
the possible outcomes P_sub_i times log
of two because we're encoding them in bits of P_sub_i.
And of course, another form of this is to use arithmetic to-
it's actually log of, one over P_sub_i.
And another form for this would be to use arithmetic
N to have minus m times the aggregation of
log of P_sub_i's times P_sub_i's,
and the Riemann sum is over all these P_sub_i times log of P_sub_i.
So again, this was constructed or established by Claude Shannon in 1948.
Previously, I talked about the efficiency of
such encoding scheme and we actually had
it so that the more probable outcomes use less number of bits.
This is commonly practiced when we want to,
in digital encoding, when we want to make that encoding scheme efficient.
For example, an example of it is Morse code.
When you want to take English letters and encode it in Morse code,
and Morse code uses two alphabets,
dots or dashes, then we take
the most probable English letters and encode them using smaller number of dots or dashes.
For example, in Morse code,
we'll encode the letter E and the letter T to have one dot
and one dash and the rest of the letters will have more than one dot or one dash.
