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On the Number of Switches in Unbiased Cointossing
, 2013
"... 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 li ..."
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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
the National Science Foundation Grant No. GU2059. UNBIASED COIN TOSSING WITH A BIASED COIN by
, 1969
"... 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 ..."
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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
Simulating Fair Dice with a Small Set of Rationally Biased 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 ..."
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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
LeakageResilient Coin Tossing
, 2011
"... The ability to collectively toss a common coin among n parties in the presence of faults is an important primitive in the arsenal of randomized distributed protocols. In the case of dishonest majority, it was shown to be impossible to achieve less than 1 bias in O(r) rounds (Cleve STOC r ’86). In th ..."
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Cited by 2 (2 self)
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). 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
Our Data, Ourselves: Privacy via Distributed Noise Generation
 In EUROCRYPT
, 2006
"... Abstract. In this work we provide efficient distributed protocols for generating shares of random noise, secure against malicious participants. The purpose of the noise generation is to create a distributed implementation of the privacypreserving statistical databases described in recent papers [14 ..."
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Cited by 152 (15 self)
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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
An Optimally Fair Coin Toss
"... We address one of the foundational problems in cryptography: the bias of coinflipping protocols. Coinflipping 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 ..."
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Cited by 15 (0 self)
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We address one of the foundational problems in cryptography: the bias of coinflipping protocols. Coinflipping 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
Development of an automated coin grader
 Proc. MASPLAS’02
, 2002
"... This paper introduces a computeroperated, rare coin grading system. In the numismatic community today the official grading of coins is accomplished by human inspection. This, by its very nature, lends itself to inconsistent results that can misrepresent the actual value of a coin by thousands of do ..."
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Cited by 2 (0 self)
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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
Counting by Coin Tossings
"... Abstract. This text is an informal review of several randomized algorithms that have appeared over the past two decades and have proved instrumental in extracting efficiently quantitative characteristics of very large data sets. The algorithms are by nature probabilistic and based on hashing. They e ..."
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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
A Full Characterization of Functions that Imply Fair Coin Tossing and Ramifications to Fairness ∗
, 2013
"... It is well known that it is impossible for two parties to toss a coin fairly (Cleve, STOC 1986). This result implies that it is impossible to securely compute with fairness any function that can be used to toss a coin fairly. In this paper, we focus on the class of deterministic Boolean functions wi ..."
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Cited by 2 (2 self)
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with finite domain, and we ask for which functions in this class is it possible to informationtheoretically 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
Unbiased Random Sequences from Quasigroup String Transformations
 In Proc. Fast Software Encryption: 12th International Workshop, FSE 2005
"... Abstract. The need of true random number generators for many purposes (ranging from applications in cryptography and stochastic simulation, to search heuristics and game playing) is increasing every day. Many sources of randomness possess the property of stationarity. However, while a biased die may ..."
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Cited by 9 (5 self)
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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
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