Cart (Loading....) | Create Account
Close category search window
 

How to turn loaded dice into fair coins

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Juels, A. ; RSA Labs., RSA Security Inc., Bedford, MA, USA ; Jakobsson, M. ; Shriver, E. ; Hillyer, B.K.

We present a new technique for simulating fair coin flips using a biased, stationary source of randomness. Sequences of random numbers are of pervasive importance in cryptography and vital to many other computing applications. Many sources of randomness, such as radioactive or quantum-mechanical sources, possess the property of stationarity. In other words, they produce independent outputs over fixed probability distributions. The output of such sources may be viewed as the result of rolling a biased or loaded die. While a biased die may be a good source of entropy, many applications require input in the form of unbiased bits, rather than biased ones. For this reason, von Neumann (1951) presented a now well-known and extensively investigated technique for using a biased coin to simulate a fair coin. We describe a new generalization of von Neumann's algorithm distinguished by its high level of practicality and amenability to analysis. In contrast to previous efforts, we are able to prove our algorithm optimally efficient, in the sense that it simulates the maximum possible number of fair coin flips for a given number of die rolls. In fact, we are able to prove that in an asymptotic sense our algorithm extracts the full entropy of its input. Moreover, we demonstrate experimentally that our algorithm achieves a high level of computational and output efficiency in a practical setting

Published in:

Information Theory, IEEE Transactions on  (Volume:46 ,  Issue: 3 )

Date of Publication:

May 2000

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.