• Bulletin of the Korean Mathematical Society
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• v.53 no.5
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• pp.1439-1446
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• 2016

The execution complexities of the major time memory data tradeoff methods are analyzed in this paper. The multi-target tradeoffs covered are the classical Hellman, distinguished point, and fuzzy rainbow methods, both in their non-perfect and perfect table versions for the latter two methods. We show that their computational complexities are identical to those of the corresponding single-target methods executed under certain matching parameters and conclude that the perfect table fuzzy rainbow tradeoff method is most preferable.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.2
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• pp.73-81
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• 2007

We give two weaknesses of a recently proposed streamcipher MICKEY. We show time-memory-data tradeoff is applicable. We also show that the state update function reduces entropy of the internal state as it is iterated, resulting in keystreams that start out differently but become merged together towards the end.

• Journal of Computing Science and Engineering
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• v.9 no.1
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• pp.1-8
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• 2015

Accessing and searching in a sequence of numbers are fundamental operations in computing that are encountered in a wide range of applications. One of the applications of the problem is cryptanalytic time-memory tradeoff which is aimed at a one-way function. A rainbow table, which is a common method for the time-memory tradeoff, contains elements from an input domain of a hash function that are normally sorted integers. In this paper, we present a practical indexing method for a monotonically increasing static sequence of numbers where the access and search queries can be addressed efficiently in terms of both time and space complexity. For a sequence of n numbers from a universe $U=\{0,{\ldots},m-1\}$, our data structure requires n lg(m/n) + O(n) bits with constant average running time for both access and search queries. We also give an analysis of the time and space complexities of the data structure, supported by experiments with rainbow tables.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.81-98
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• 2013

Since Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) were introduced, many revised or extended topic models have appeared. Due to the intractable likelihood of these models, training any topic model requires to use some approximation algorithm such as variational approximation, Laplace approximation, or Markov chain Monte Carlo (MCMC). Although these approximation algorithms perform well, training a topic model is still computationally expensive given the large amount of data it requires. In this paper, we propose a new method, called non-simultaneous sampling deactivation, for efficient approximation of parameters in a topic model. While each random variable is normally sampled or obtained by a single predefined burn-in period in the traditional approximation algorithms, our new method is based on the observation that the random variable nodes in one topic model have all different periods of convergence. During the iterative approximation process, the proposed method allows each random variable node to be terminated or deactivated when it is converged. Therefore, compared to the traditional approximation ways in which usually every node is deactivated concurrently, the proposed method achieves the inference efficiency in terms of time and memory. We do not propose a new approximation algorithm, but a new process applicable to the existing approximation algorithms. Through experiments, we show the time and memory efficiency of the method, and discuss about the tradeoff between the efficiency of the approximation process and the parameter consistency.