A biased coin is tossed n times independently and sequentially. A “head ” switch is a tail followed by a head and a “tail ” switch is a head followed by a tail. Joint Laplace transform for the number of “head ” switches and “tail ” switches are given. For the total number of switches, the central

O. SUffi1.Uary. PrQcedures are exhibited and analyzed for converting a sequence of iid Bernoulli variables with unknown mean p into a Bernoulli variable with mean 1/2. The efficiency of several procedures is studied. 1. Introduction. Coins

1.1 Background and Motivation A problem of simulating fair dice with coins is initiated by Feldman et al [Fetal]. Informally, the problem can be defined as follows: Let $n\geq 2 $ be an integer. Given a set of $m\geq 1 $ (biased or unbiased) coins, output with equal probability 1, 2,.., , $n $ in a

). In the case of honest majority, in contrast, unconditionally secure O(1)-round protocols for generating common perfectly unbiased coins follow from general completeness theorems on multiparty secure protocols in the perfectly secure channels model (e.g., BGW, CCD STOC ’88). However, in the multi

introduces a technique for distributing shares of many unbiased coins with fewer executions of verifiable secret sharing than would be needed using previous approaches (reduced by afactorofn). The generation of exponentially distributed noise uses two shallow circuits: one for generating many arbitrarily

We address one of the foundational problems in cryptography: the bias of coin-flipping protocols. Coin-flipping protocols allow mutually distrustful parties to generate a common unbiased random bit, guaranteeing that even if one of the parties is malicious, it cannot significantly bias the output

of dollars, and the human element can further misrepresent for personal gain. Therefore, an automated computer grading system should be a welcome unbiased alternative. To demonstrate its potential utility in this application we are developing a system that grades, appraises, and authenticates rare coins

algorithms. Characteristics like the total number of elements, cardinality (the number of distinct elements), frequency moments, as well as unbiased samples can be gathered with little loss of information and only a small probability of failure. The algorithms are applicable to traffic monitoring in networks

with finite domain, and we ask for which functions in this class is it possible to information-theoretically toss an unbiased coin, given a protocol for securely computing the function with fairness. We provide a complete characterization of the functions in this class that imply and do not imply fair coin

may be a good source of entropy, many applications require input in the form of unbiased bits, rather than biased ones. In this paper, we present a new technique for simulating fair coin flips using a biased, stationary source of randomness. Moreover, the same technique can also be used to improve