Results 1 
7 of
7
A Study of UserFriendly Hash Comparison Schemes
, 2009
"... Several security protocols require a human to compare two hash values to ensure successful completion. When the hash values are represented as long sequences of numbers, humans may make a mistake or require significant time and patience to accurately compare the hash values. To improve usability du ..."
Abstract

Cited by 12 (0 self)
 Add to MetaCart
Several security protocols require a human to compare two hash values to ensure successful completion. When the hash values are represented as long sequences of numbers, humans may make a mistake or require significant time and patience to accurately compare the hash values. To improve usability during comparison, a number of researchers have proposed various hash representations that use words, sentences, or images rather than numbers. This is the first work to perform a comparative study of these hash comparison schemes to determine which scheme allows the fastest and most accurate comparison. To evaluate the schemes, we performed an online user study with more than 400 participants. Our findings indicate that only a small number of schemes allow quick and accurate comparison across a wide range of subjects from varying backgrounds.
A Study of UserFriendly Hash Comparison Schemes
"... Abstract—Several security protocols require a human to compare two hash values to ensure successful completion. When the hash values are represented as long sequences of numbers, humans may make a mistake or require significant time and patience to accurately compare the hash values. To improve usab ..."
Abstract
 Add to MetaCart
Abstract—Several security protocols require a human to compare two hash values to ensure successful completion. When the hash values are represented as long sequences of numbers, humans may make a mistake or require significant time and patience to accurately compare the hash values. To improve usability during comparison, a number of researchers have proposed various hash representations that use words, sentences, or images rather than numbers. This is the first work to perform a comparative study of these hash comparison schemes to determine which scheme allows the fastest and most accurate comparison. To evaluate the schemes, we performed an online user study with more than 400 participants. Our findings indicate that only a small number of schemes allow quick and accurate comparison across a wide range of subjects from varying backgrounds. KeywordsSecurity; Human factors I.
Opportunistic Sampling for Joint Population Size and Density Estimation
"... Abstract—Consider a set of probes, called “agents”, who sample, based on opportunistic contacts, a population moving between a set of discrete locations. An example of such agents are Bluetooth probes that sample the visible Bluetooth devices in a population. Based on the obtained measurements, we c ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract—Consider a set of probes, called “agents”, who sample, based on opportunistic contacts, a population moving between a set of discrete locations. An example of such agents are Bluetooth probes that sample the visible Bluetooth devices in a population. Based on the obtained measurements, we construct a parametric statistical model to jointly estimate the total population size (e.g., the number of visible Bluetooth devices) and their spatial density. We evaluate the performance of our estimators by using Bluetooth traces obtained during an openair event and WiFi traces obtained on a university campus. Index Terms—Population size and density estimation, opportunistic sampling, Bluetooth sampling Ç 1
Patterns of i.i.d. Sequences and Their Entropy Part II: Bounds for Some Distributions ∗
, 711
"... A pattern of a sequence is a sequence of integer indices with each index describing the order of first occurrence of the respective symbol in the original sequence. In a recent paper, tight general bounds on the block entropy of patterns of sequences generated by independent and identically distribu ..."
Abstract
 Add to MetaCart
(Show Context)
A pattern of a sequence is a sequence of integer indices with each index describing the order of first occurrence of the respective symbol in the original sequence. In a recent paper, tight general bounds on the block entropy of patterns of sequences generated by independent and identically distributed (i.i.d.) sources were derived. In this paper, precise approximations are provided for the pattern block entropies for patterns of sequences generated by i.i.d. uniform and monotonic distributions, including distributions over the integers, and the geometric distribution. Numerical bounds on the pattern block entropies of these distributions are provided even for very short blocks. Tight bounds are obtained even for distributions that have infinite i.i.d. entropy rates. The approximations are obtained using general bounds and their derivation techniques. Conditional index entropy is also studied for distributions over smaller alphabets. Index Terms: patterns, monotonic distributions, uniform distributions, entropy.
Universal algorithms: building a case for pointwise convergence
"... Abstract — We consider algorithms for prediction, compression and entropy estimation in a universal setup. In each case, we estimate some function of an unknown distribution p over the set of natural numbers, using only n observations generated i.i.d. from p. While p is unknown, it belongs to a kno ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract — We consider algorithms for prediction, compression and entropy estimation in a universal setup. In each case, we estimate some function of an unknown distribution p over the set of natural numbers, using only n observations generated i.i.d. from p. While p is unknown, it belongs to a known collection P of possible models. When the supports of distributions in P are uniformly bounded, consistent algorithms exist for each of the problems. Namely, the convergence of the estimate to the true value can be bounded by a function depending only on the sample size, n, and not on the underlying distribution p. However, when the supports of distributions in P are not uniformly bounded, a more natural approach involves algorithms that are pointwise consistent, namely, the convergence to the true value is at a rate that depends on both n and the underlying (unknown) distribution p. The obvious practical difficulty with pointwise convergence is that the asymptotic consistency of the algorithm may indicate nothing about the performance of the algorithm for any fixed sample size, since the underlying distribution is unknown. In this paper, we first note that for many complex model classes P, we can still circumvent the above practical difficulty with pointwise convergence. Secondly, we take here a preliminary step towards characterizing a broad framework establishing the hierarchy of difficulty of problems involving pointwise convergence. We look for connections among the following problems which we define for a pointwise convergence scenario: (i) predicting good upper bounds on the next unseen sample, (ii) weak universal compression, and (iii) entropy estimation. We construct counterexamples to show that no two properties above imply the third.
Pattern Entropy Revisited
"... Abstract A pattern of a sequence is a sequence of integer indices with each index describing the order of rst occurrence of the respective symbol in the original sequence. Several recent works studied entropy and entropy rate of patterns. Specically, in a recent paper, tight general bounds on the b ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract A pattern of a sequence is a sequence of integer indices with each index describing the order of rst occurrence of the respective symbol in the original sequence. Several recent works studied entropy and entropy rate of patterns. Specically, in a recent paper, tight general bounds on the block entropy of patterns of sequences generated by independent and identically distributed (i.i.d.) sources were derived. In this paper, precise approximations are given to the pattern block entropies for patterns of sequences generated by i.i.d. uniform and monotonic distributions, including distributions over the integers, and the geometric distribution. Numerical nonasymptotic bounds on the pattern block entropies of these distributions are provided even for very short blocks, and even for distributions that have innite i.i.d. entropy rates. Conditional index entropy is also studied for distributions over smaller alphabets.