How about deleting confidential data from hard disks!

The solution today is to overwrite it many times with random data.

But modern mathematics and technology makes it possible to recover the much
of the original text given the original random sequence used to delete the
data. Given a long sequence of deleted white space (or zeros on the disk),
then it becomes possible to recover the original pseudo-random sequence (for
example, one based on linear
<http://en.wikipedia.org/wiki/Linear_congruence_theorem>  congruence) - even
if a many passes are performed.

With a true random number generator, only one pass is needed.

I'm sure hardware random number generators based on quantum effects, which
have been around for decades, would be used instead of hitting a web site,
which compromises the who shebang.

 

Robert Howard

Phoenix, Arizona

 

 

 

-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf
Of Roger Frye
Sent: Sunday, July 22, 2007 6:40 AM
To: The Friday Morning Applied Complexity Coffee Group
Subject: Re: [FRIAM] Why "true" random?

 

I would argue the opposite.  While I agree with Doug that you need good  

RNGs (though not necessarily true RNGs) in order to avoid bias, the  

problem with good pseudo- or true- RNGs is that they have order N^2  

convergence for Monte Carlo simulations.  Quasi-random number generators  

on the other hand (such as multiples of an irrational square root, or a  

Peano tiling) converge in order N.  If you can trust the results, faster  

conergence lets you simulate more.

-Roger

 

On Sat, 21 Jul 2007 23:18:36 -0600, Douglas Roberts <[EMAIL PROTECTED]>  

wrote:

 

> Simulations of stochastic processes also require good RN generators,

> especially for simulations of large systems with (I hate to use this  

> word)

> emergent behavioral properties.  A bad RN generator will introduce  

> emergent

> behavior that will be "flavored" by a bad random sequences.

> 

> 

 

 

 

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