• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.18 no.6A
• /
• pp.139-149
• /
• 2008

The amount of personal information collected by organizations and government agencies is continuously increasing. When a data collector publishes personal information for research and other purposes, individuals' sensitive information should not be revealed. On the other hand, published data is also required to provide accurate statistical information for analysis. k-Anonymity and ${\iota}$-diversity models are popular approaches for privacy preserving data publication. However, they are limited to static data release. After a dataset is updated with insertions and deletions, a data collector cannot safely release up-to-date information. Recently, the m-invariance model has been proposed to support re-publication of dynamic datasets. However, the m-invariant generalization can cause high information loss. In addition, if the adversary already obtained sensitive values of some individuals before accessing released information, the m-invariance leads to severe privacy disclosure. In this paper, we propose a novel technique for safely releasing dynamic datasets. The proposed technique offers a simple and effective method for handling inserted and deleted records without generalization. It also gives equivalent degree of privacy preservation to the m-invariance model.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.19 no.1
• /
• pp.43-51
• /
• 2009

In 1998, Kocher et al. introduced Differential Power Attack on block ciphers. This attack allows to extract secret key used in cryptographic primitives even if these are executed inside tamper-resistant devices such as smart card. At FSE 2003 and 2004, Akkar and Goubin presented several masking methods, randomizing the first few and last few($3{\sim}4$) rounds of the cipher with independent random masks at each round and thereby disabling power attacks on subsequent inner rounds, to protect iterated block ciphers such as DES against Differential Power Attack. Since then, Handschuh and Preneel have shown how to attack Akkar's masking method using Differential Cryptanalysis. This paper presents how to combine Truncated Differential Cryptanalysis and Power Attack to extract the secret key from intermediate unmasked values and shows how much more efficient our attacks are implemented than the Handschuh-Preneel method in term of reducing the number of required plaintexts, even if some errors of Hamming weights occur when they are measured.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.12 no.2
• /
• pp.21-34
• /
• 2002

In this paper we propos a new hardware architecture of modular exponentiation using a division chain method which has been proposed in (2). Modular exponentiation using the division chain is performed by receding an exponent E as a mixed form of multiplication and addition with divisors d=2 or $d=2^I +1$ and respective remainders r. This calculates the modular exponentiation in about $1.4log_2$E multiplications on average which is much less iterations than $2log_2$E of conventional Binary Method. We designed a linear systolic array multiplier with pipelining and used a horizontal projection on its data dependence graph. So, for k-bit key, two k-bit data frames can be inputted simultaneously and two modular multipliers, each consisting of k/2+3 PE(Processing Element)s, can operate in parallel to accomplish 100% throughput. We propose a new encoding scheme to represent divisors and remainders of the division chain to keep regularity of the data path. When it is synthesized to ASIC using Samsung 0.5 um CMOS standard cell library, the critical path delay is 4.24ns, and resulting performance is estimated to be abort 140 Kbps for a 1024-bit data frame at 200Mhz clock In decryption process, the speed can be enhanced to 560kbps by using CRT(Chinese Remainder Theorem). Futhermore, to satisfy real time requirements we can choose small public exponent E, such as 3,17 or $2^{16} +1$, in encryption and verification process. in which case the performance can reach 7.3Mbps.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.12 no.6
• /
• pp.17-28
• /
• 2002

XTR is a new method to represent elements of a subgroup of a multiplicative group of a finite field GF( $p^{6m}$) and it can be generalized to the field GF( $p^{6m}$)$^{[6,9]}$ This paper progress optimal extention fields for XTR among Galois fields GF ( $p^{6m}$) which can be aplied to XTR. In order to select such fields, we introduce a new notion of Generalized Opitimal Extention Fields(GOEFs) and suggest a condition of prime p, a defining polynomial of GF( $p^{2m}$) and a fast method of multiplication in GF( $p^{2m}$) to achieve fast finite field arithmetic in GF( $p^{2m}$). From our implementation results, GF( $p^{36}$ )longrightarrowGF( $p^{12}$ ) is the most efficient extension fields for XTR and computing Tr( $g^{n}$ ) given Tr(g) in GF( $p^{12}$ ) is on average more than twice faster than that of the XTR system on Pentium III/700MHz which has 32-bit architecture.$^{［6,10］/ ［6,10］/6,10］}$

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.13 no.1
• /
• pp.47-57
• /
• 2003

In this paper, we firstly evaluate the resistance of the reduced 5-round version of the block cipher CIKS-1 against linear cryptanalysis(LC) and show that we can attack full-round CIKS-1 with \ulcorner56-bit key through the canonical extension of our attack. A feature of the CIKS-1 is the use of both Data-Dependent permutations(DDP) and internal key scheduling which consist in data dependent transformation of the round subkeys. Taking into accout the structure of CIKS-1 we investigate linear approximation. That is, we consider 16 linear approximations with p=3/4 for 16 parallel modulo $2^2$ additions to construct one-round linear approximation and derive one-round linear approximation with the probability P=1/2+$2^{-17}$ by Piling-up lemma. Then we present 3-round linear approximation with 1/2+$2^{-17}$ using this one-round approximation and attack the reduced 5-round CIKS-1 with 64-bit block by LC. In conclusion we present that our attack requires $2^{38}$chosen plaintexts with a probability of success of 99.9% and about $2^{67-7}$encryption times to recover the last round key.(But, for the full-round CIKS-1, our attack requires about $2^{166}$encryption times)

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.19 no.6
• /
• pp.23-35
• /
• 2009

Password-Authenticated Key Exchange (PAKE) Protocol is a useful tool for secure communication conducted over open networks without sharing a common secret key or assuming the existence of the public key infrastructure (PKI). It seems difficult to design efficient PAKE protocols using RSA, and thus many PAKE protocols are designed based on the Diffie-Hellman key exchange (DH-PAKE). Therefore it is important to design an efficient PAKE based on RSA function since the function is suitable for designing a PAKE protocol for imbalanced communication environment. In this paper, we propose a computationally-efficient key exchange protocol based on the RSA function that is suitable for low-power devices in imbalanced environment. Our protocol is more efficient than previous RSA-PAKE protocols, required theoretical computation and experiment time in the same environment. Our protocol can provide that it is more 84% efficiency key exchange than secure and the most efficient RSA-PAKE protocol CEPEK. We can improve the performance of our protocol by computing some costly operations in offline step. We prove the security of our protocol under firmly formalized security model in the random oracle model.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.1
• /
• pp.153-166
• /
• 2011

Recent trends in mobile device market whose services are rapidly expanding to provide wireless internet access are drawing people's attention to mobile security. Especially, since threats to information leakage are reaching to the critical level due to the frequent interchange of important data such as personal and financial information through wireless internet, various encryption algorithms has been developed to protect them. The encryption algorithms confront the serious threats by the appearance of side channel attack (SCA) which uses the physical leakage information such as timing, and power consumption, though the their robustness to threats is theoretically verified. Against the threats of SCA, researches including the performance and development direction of SCA should precede. Among tile SCA methods, the power analysis (PA) attack overcome this misalignment problem. The conventional methods require large computational power and they do not effectively deal with the delay changes in a power trace. To overcome the limitation of the conventional methods, we proposed a novel alignment method using peak matching. By computer simulations, we show the advantages of the proposed method compared to the conventional alignment methods.

• Journal of IKEEE
• /
• v.25 no.2
• /
• pp.243-251
• /
• 2021

In this paper, we propose a measurement technique of indoor location based on markerless applicable to AR. The proposed technique has the following originality. The first is to extract feature points and use them to generate local patches to enable faster computation by learning and using only local patches that are more useful than the surroundings without learning the entire image. Second, learning is performed through deep learning using the convolution neural network structure to improve accuracy by reducing the error rate. Third, unlike the existing feature point matching technique, it enables indoor location measurement including left and right movement. Fourth, since the indoor location is newly measured every frame, errors occurring in the front side during movement are prevented from accumulating. Therefore, it has the advantage that the error between the final arrival point and the predicted indoor location does not increase even if the moving distance increases. As a result of the experiment conducted to evaluate the time required and accuracy of the measurement technique of indoor location based on markerless applicable to AR proposed in this paper, the difference between the actual indoor location and the measured indoor location is an average of 12.8cm and a maximum of 21.2cm. As measured, the indoor location measurement accuracy was better than that of the existing IEEE paper. In addition, it was determined that it was possible to measure the user's indoor location in real time by displaying the measured result at 20 frames per second.

• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.3
• /
• pp.148-153
• /
• 2020

In order to obtain high accuracy with an convolutional neural network(CNN), it is necessary to set the optimal hyperparameters. However, the exact value of the hyperparameter that can make high performance is not known, and the optimal hyperparameter value is different based on the type of the dataset, therefore, it is necessary to find it through various experiments. In addition, since the range of hyperparameter values is wide and the number of combinations is large, it is necessary to find the optimal values of the hyperparameters after the experimental design in order to save time and computational costs. In this paper, we suggest an algorithm that use the design of experiments and grid search algorithm to determine the optimal hyperparameters for a classification problem. This algorithm determines the optima values of the hyperparameters that yields high performance using the factorial design of experiments. It is shown that the amount of computational time can be efficiently reduced and the accuracy can be improved by performing a grid search after reducing the search range of each hyperparameter through the experimental design. Moreover, Based on the experimental results, it was shown that the learning rate is the only hyperparameter that has the greatest effect on the performance of the model.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.6
• /
• pp.1291-1300
• /
• 2020

Recently, a deep learning-based non-profiling side-channel analysis was proposed. The deep learning-based non-profiling analysis is a technique that trains a neural network model for all guessed keys and then finds the correct secret key through the difference in the training metrics. As the performance of non-profiling analysis varies greatly depending on the neural network training model design, a correct model design criterion is required. This paper describes the two types of loss functions and eight labeling methods used in the training model design. It predicts the analysis performance of each labeling method in terms of non-profiling analysis and power consumption model. Considering the characteristics of non-profiling analysis and the HW (Hamming Weight) power consumption model is assumed, we predict that the learning model applying the HW label without One-hot encoding and the Correlation Optimization (CO) loss will have the best analysis performance. And we performed actual analysis on three data sets that are Subbytes operation part of AES-128 1 round. We verified our prediction by non-profiling analyzing two data sets with a total 16 of MLP-based model, which we describe.