• 대한수학회보
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• 제53권5호
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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.

• 정보보호학회논문지
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• 제17권2호
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• pp.73-81
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• 2007

본 논문에서는 스트림 암호 MICKEY의 두 가지 취약점에 대해서 논한다. 첫째, time-memory-data tradeoff 공격이 가능함을 보인다. 둘째, 상태 갱신 함수 (state update function)를 반복해서 적용할수록 내부 상태 (internal state)의 엔트로피가 감소하므로 다르게 시작된 키 스트림이 마침내 같아질 수 있다.

• Journal of Computing Science and Engineering
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• 제9권1호
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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.

• 대한수학회지
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• 제52권4호
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• pp.797-819
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• 2015

A trapdoor discrete logarithm group is a cryptographic primitive with many applications, and an algorithm that allows discrete logarithm problems to be solved faster using a pre-computed table increases the practicality of using this primitive. Currently, the distinguished point method and one extension to this algorithm are the only pre-computation aided discrete logarithm problem solving algorithms appearing in the related literature. This work investigates the possibility of adopting other pre-computation matrix structures that were originally designed for used with cryptanalytic time memory tradeoff algorithms to work as pre-computation aided discrete logarithm problem solving algorithms. We find that the classical Hellman matrix structure leads to an algorithm that has performance advantages over the two existing algorithms.

• Journal of Power Electronics
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• 제19권6호
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• pp.1429-1439
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• 2019

Buck-Boost LLC (BBLLC) converters based on a PWM + phase control strategy are good candidates for high efficiency, high power density and wide input range applications. Nevertheless, they suffer from large computational complexity when it comes to calculating the optimal phase for ZVS of all the switches. In this paper, a method is proposed for a microcontroller unit (MCU) to calculate the optimal phase quickly and accurately. Firstly, a 2-D lookup table of the phase is established with an index of the input voltage and output current. Then, a bilinear interpolation method is applied to improve the accuracy. Meanwhile, simplification of the phase equation is presented to reduce the computational complexity. When compared with conventional curve-fitting and LUT methods, the proposed method makes the best tradeoff among the accuracy of the optimal phase, the computation time and the memory consumption of the MCU. Finally, A 350V-420V input, 24V/30A output experimental prototype is built to verify the proposed method. The efficiency can be improved by 1% when compared with the LUT method, and the computation time can be reduced by 13.5% when compared with the curve-fitting method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권1호
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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.