Re: Playing Cards - Caution!

[ph@netcom.com (Peter Hendrickson)]

At 10:57 AM 11/19/1996, Alan Bostick wrote:
> Thirty-two bits of randomness in a space that is 225 bits wide leave
> room for an awful lot of order.

The maximum entropy after one riffle shuffle is actually about
48 bits, assuming the deck was split in half.  That is, n! / k! (n-k)! =
52! / 2 * 26! ~= 4.9592 * 10^14.  log_2 4.9592 * 10^14 = 48 bits.
(Think of one half of the deck of k cards fitting into n possible
holes.  The order of each half of the deck is guaranteed, so this
can be thought of as the number of ways k balls fit into n holes.
What about the other half of the deck?  They just fall into the
empty holes.)

Assume the split introduces about 4 more bits, and we get 52 bits
of possible shuffles.  However, in practice a riffle shuffle may be
more predictable.

It is possible that 32 bits of entropy are introduced with each riffle
shuffle, but it is not obviously true.  For a cryptographic application,
the standard is higher than for almost any other.  So, if it is not
clear that something is the case, probably it shouldn't be used.

>> A model is presented which Diaconis believes is similar to how people
>> shuffle in real life.  What is troubling from a cryptographic point of
>> view is that there is little empirical evidence to back this up.  What
>> is more, Diaconis mentions that there is some variation in shufflers.
>> A neat shuffler will be less random.  (Side note: The Economist claims
>> Diaconis can execute 8 perfect shuffles in less than a minute.  This
>> means the deck is returned to its original order!)

> Mathematics does not rely on empirical evidence. ;-)

That's true, but the application of mathematics certainly does
depend on empirical evidence.  I would be very surprised if Diaconis
made a mistake with the math.  I think the math is what primarily
interests him.  From his book I did not see a lot of evidence that
a great deal of empirical research on card shuffling had been performed.
Rather, a few mathematicians here and there appear to have tried out
the model and decided that it more or less works.  For the standards
we should apply, those of a cryptographic application, this is not
good enough.

>> The study of randomness in cards looks much harder to me now.  Also,
>> flaws which may be exploitable for financial reasons when real money
>> is on the table may have to be substantially more dramatic than the
>> flaws required to exploit, for instance, an alleged one-time pad.

> Do bear in mind that, unless you distill its entropy through hashing,
> a randomly-ordered deck of cards is going to show a lot of seemingly
> non-random properties if you try to use it as a one-time pad.  Most
> obviously, because each card in the deck is dealt once and only once,
> there must of necessity be correlations between cards dealt early in
> the deck and cards dealt later.  (Card-counters at blackjack tables
> make their money by exploiting these correlations.)

I solved this problem in my original message on the subject.  You use
the order of the cards to represent your message.  Then you use a
fully shuffled deck to mix up the mapping of the cards to numbers.  If
the deck is truly randomly ordered, the scheme I proposed is a true
one time pad.  (If I made an error, I would like to hear about it!)

Tangentially, I would be reluctant to rely on a one time pad whose
key was produced by a hash function.  We assume that the hash function
is cryptographically secure, but if we can assume that, we might as
well assume that some cryptosystem is secure.  One time pads are supposed
to avoid those types of assumptions.

> The randomness of rolling dice is much more easy to interpret than that
> of dealt cards.

I agree completely.  In practice, you pretty much have to have a
computer to make use of the original schemes I proposed.

The literature on card shuffling is quite interesting.  A number of people
over many years have worked on this.  It looks to me like there's lots
more interesting work to be done.  Many readers of this list would
probably find Diaconis's book interesting.

There might even be a number of new attacks possible on card games.
If 7 riffle shuffles does not, in fact, bring the deck to complete
entropy it means that a number of combinations of cards are unlikely.
Is the distribution of pokers hands perfectly even?  They may not
be.  Solving this problem in the general case appears to be very
hard.  But, some progress could be made by modeling real shuffling
and looking for patterns in the relationships between the cards
electronically.  Conceivably, poker hands are not as random as people
believe.  While the cards you have in your hand may be random, their
relationship to other cards may not.

This is like a pseudo random number generator.  If you don't notice
the pattern, you will believe you are looking at a random number
stream, but in fact you are not.

However, the entire subject is orthogonal to my goals right now.

Peter Hendrickson
ph@netcom.com