• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.4
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• pp.63-70
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• 2003

In the modern electronic world, the authentication of a person is an important task in many areas of online-transactions. Using biometrics to authenticate a person's identity has several advantages over the present practices of Personal Identification Numbers(PINs) and passwords. To gain maximum security in the verification system using biometrics, the computation of the verification as well as the store of the biometric pattern has to be taken place in the security token(smart card, USB token). However, there is an open issue of integrating biometrics into the security token because of its limited resources(memory space, processing power). In this paper, we describe our implementation of the USB security token system having 206MHz StrongARM CPU, 16MBytes flash memory, and 1MBytes RAM. Also, we evaluate the performance of a light-weighted In-gerprint verification algorithm that can be executed in the restricted environments. Based on experimental results, we confirmed that the RAM requirement of the proposed algorithm was about 6.8 KBytes and the Equal Error Rate(EER) was 1.7%.

• Asia pacific journal of information systems
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• v.22 no.3
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• pp.53-74
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• 2012

A challenge in fostering virtual communities is the continuous supply of knowledge, namely members' willingness to contribute knowledge to their communities. Previous research argues that giving away knowledge eventually causes the possessors of that knowledge to lose their unique value to others, benefiting all except the contributor. Furthermore, communication within virtual communities involves a large number of participants with different social backgrounds and perspectives. The establishment of mutual understanding to comprehend conversations and foster knowledge contribution in virtual communities is inevitably more difficult than face-to-face communication in a small group. In spite of these arguments, evidence suggests that individuals in virtual communities do engage in social behaviors such as knowledge contribution. It is important to understand why individuals provide their valuable knowledge to other community members without a guarantee of returns. In virtual communities, knowledge is inherently rooted in individual members' experiences and expertise. This personal nature of knowledge requires social interactions between virtual community members for knowledge transfer. This study employs the social capital theory in order to account for interpersonal relationship factors and identity theory for individual and group factors that may affect knowledge contribution. First, social capital is the relationship capital which is embedded within the relationships among the participants in a network and available for use when it is needed. Social capital is a productive resource, facilitating individuals' actions for attainment. Nahapiet and Ghoshal (1997) identify three dimensions of social capital and explain theoretically how these dimensions affect the exchange of knowledge. Thus, social capital would be relevant to knowledge contribution in virtual communities. Second, existing research has addressed the importance of identity in facilitating knowledge contribution in a virtual context. Identity in virtual communities has been described as playing a vital role in the establishment of personal reputations and in the recognition of others. For instance, reputation systems that rate participants in terms of the quality of their contributions provide a readily available inventory of experts to knowledge seekers. Despite the growing interest in identities, however, there is little empirical research about how identities in the communities influence knowledge contribution. Therefore, the goal of this study is to better understand knowledge contribution by examining the roles of social capital and identity in virtual communities. Based on a theoretical framework of social capital and identity theory, we develop and test a theoretical model and evaluate our hypotheses. Specifically, we propose three variables such as cohesiveness, reciprocity, and commitment, referring to the social capital theory, as antecedents of knowledge contribution in virtual communities. We further posit that members with a strong identity (self-presentation and group identification) contribute more knowledge to virtual communities. We conducted a field study in order to validate our research model. We collected data from 192 members of virtual communities and used the PLS method to analyse the data. The tests of the measurement model confirm that our data set has appropriate discriminant and convergent validity. The results of testing the structural model show that cohesion, reciprocity, and self-presentation significantly influence knowledge contribution, while commitment and group identification do not significantly influence knowledge contribution. Our findings on cohesion and reciprocity are consistent with the previous literature. Contrary to our expectations, commitment did not significantly affect knowledge contribution in virtual communities. This result may be due to the fact that knowledge contribution was voluntary in the virtual communities in our sample. Another plausible explanation for this result may be the self-selection bias for the survey respondents, who are more likely to contribute their knowledge to virtual communities. The relationship between self-presentation and knowledge contribution was found to be significant in virtual communities, supporting the results of prior literature. Group identification did not significantly affect knowledge contribution in this study, inconsistent with the wealth of research that identifies group identification as an important factor for knowledge sharing. This conflicting result calls for future research that examines the role of group identification in knowledge contribution in virtual communities. This study makes a contribution to theory development in the area of knowledge management in general and virtual communities in particular. For practice, the results of this study identify the circumstances under which individual factors would be effective for motivating knowledge contribution to virtual communities.

• Analytical Science and Technology
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• v.29 no.3
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• pp.142-153
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• 2016

In crime scene investigation, fingerprint identification is regarded to be one of the most important techniques for personal identification. However, objective and unbiased evaluation methods that would compare the fingerprints with diverse available and developing methods are currently lacking. To develop an objective and quantitative method to improve fingerprint evaluation, a preliminary study was performed to extract useful research information from the analysis with densitometric image analysis (CP Atlas 2.0) and the Automated Fingerprint Identification System (AFIS) for the developed fingerprints on porous surfaces. First, inked fingerprints obtained by varying pressure (kg.f) and pressing time (sec.) to find optimal conditions for obtaining fingerprint samples were analyzed, because they could provide fingerprints of a relatively uniform quality. The extracted number of minutiae from the analysis with AFIS was compared with the calculated areas of friction ridge peaks from the image analysis. Inked fingerprints with a pressing pressure of 1.0 kg.f for 5 seconds provided the most visually clear fingerprints, the highest number of minutiae points, and the largest average area of the peaks of the friction ridge. In addition, the images of the developed latent fingerprints on thermal paper with the iodine fuming method were analyzed. Fingerprinting condition of 1.0 kg.f/5 sec was also found to be optimal when generating highest minutiae number and the largest average area of peaks of ridges. Additionally, when the concentration of ninhydrin solution (0.5 % vs. 5 %) was used to compare the developed latent fingerprints on print paper, the best fingerprinting condition was 2.0 kg.f/5 sec and 5 % of ninhydrin concentration. It was confirmed that the larger the average area of the peaks generated by the image analysis, the higher the number of minutiae points was found. With additional tests for fingerprint evaluation using the densitometric image analysis, this method can prove to be a new quantitative and objective assessment method for fingerprint development.

• The KIPS Transactions:PartD
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• v.17D no.2
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• pp.167-174
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• 2010

When entering the PIN(personal identification number), the greatest security threat is shoulder surfing attack. Shoulder surfing attack is watching the PIN being entered from over the shoulder to obtain the number, and it is the most common and at the same time the most powerful security threat of stealing the PIN. In this paper, a psychology based PINpad technology referred to as DAS(Dynamic Authentication System) that safeguards from shoulder surfing attack was proposed. Also, safety of the proposed DAS from shoulder surfing attack was tested and verified through intuitive viewpoint, shoulder surfing test, and theoretical analysis. Then, a PINpad with an internal DAS that was certified for its safety from shoulder surfing attack was designed and produced. Because the designed PINpad significantly decreases the chances for shoulder surfing attackers being able to steal the PIN when compared to the ordinary PINpad, it was determined to be suitable for use at ATM(automated teller machine)s operated by banks and therefore has been introduced and is being used by many financial institutions.

• Journal of Korean Medical Science
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• v.33 no.53
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• pp.343.1-343.8
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• 2018

Background: Linkage of public healthcare data is useful in stroke research because patients may visit different sectors of the health system before, during, and after stroke. Therefore, we aimed to establish high-quality big data on stroke in Korea by linking acute stroke registry and national health claim databases. Methods: Acute stroke patients (n = 65,311) with claim data suitable for linkage were included in the Clinical Research Center for Stroke (CRCS) registry during 2006-2014. We linked the CRCS registry with national health claim databases in the Health Insurance Review and Assessment Service (HIRA). Linkage was performed using 6 common variables: birth date, gender, provider identification, receiving year and number, and statement serial number in the benefit claim statement. For matched records, linkage accuracy was evaluated using differences between hospital visiting date in the CRCS registry and the commencement date for health insurance care in HIRA. Results: Of 65,311 CRCS cases, 64,634 were matched to HIRA cases (match rate, 99.0%). The proportion of true matches was 94.4% (n = 61,017) in the matched data. Among true matches (mean age 66.4 years; men 58.4%), the median National Institutes of Health Stroke Scale score was 3 (interquartile range 1-7). When comparing baseline characteristics between true matches and false matches, no substantial difference was observed for any variable. Conclusion: We could establish big data on stroke by linking CRCS registry and HIRA records, using claims data without personal identifiers. We plan to conduct national stroke research and improve stroke care using the linked big database.

• Tuberculosis and Respiratory Diseases
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• v.68 no.3
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• pp.146-154
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• 2010

Background: The increasing incidence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) has become a serious worldwide problem. However, there is insufficient data regarding the current status of MDR-TB and XDR-TB in Korea. This study examined the recent status of MDR- and XDR-TB using the data from 7 laboratories, in which almost all drug susceptibility tests (DST) for Mycobacterium tuberculosis were performed. Methods: The patients' identification data and DST results were collected from all 7 laboratories from 2001 to 2006 and the number of patients with MDR-TB and XDR-TB were calculated. Results: The number of DSTs was 140,638 for 6 years with an increasing incidence each year (p<0.001). The number of DST with MDR results was 18,510 and personal identifying information was obtained in 16,640 (89.9%) tests. The number of MDR-TB patients from 2001 to 2006 was 2,329, 2,496, 2,374, 2,300, 2,354, and 2,178, respectively, when counting the duplications in a year as one patient. The number of MDR-TB patients when counting the duplications in 6 years as one patient was 2,281, 1,977, 1,620, 1,446, 1,512, and 1,373, respectively. When the same method was adopted, the number of XDR-TB patients was 191, 238, 282, 260, 272, and 264, respectively, and 189, 150, 130, 90, 122, and 110 patients, respectively. Conclusion: Despite the national efforts to control TB, there are still a large number of MDR- and XDR-TB patients in Korea.

• Asia Marketing Journal
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• v.14 no.3
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• pp.69-102
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• 2012

Owing to increased number of luxury brand users, both genuine and counterfeit luxury product consumption continues to increase every year. Luxury brand is defined as use or display of a particular branded products which brings the ownership prestige apart from its functional utility(Grossmand and Shapiro 1988). Some luxury brands have imitations sold in marketplace due to their popularity. These imitations or counterfeits have been jumping on the bandwagon of the upturn in sales of their originals. The purpose of our study is to understand consumer's underlying motives to consume luxury brands, genuine and or counterfeits. To do this, we propose functional theories of attitudes, decision-making styles, and life attitudes to form the determining causes for different consumption choices of luxury brands: genuine brands, counterfeit brands, both genuine and counterfeit brands, and no consumption on luxury brands types. In proposed causal pathways, we examine moderated effects of socio-psychological factors to further investigate if consumer profiles would exert influences in causal relationships. From the existing theories of functional attitudes: value-expressive and social-adjustive attitudes, we developed and introduced a new measure of rationality-consumptive attitude. From the existing eight decision-making characteristics of consumer styles inventory(CSI), three measures of high-quality, hedonic-shopping, and price-shopping styles were primarily applied in the study along with newly introduced measure of 'high-price' being added, which makes four total. Seven life attitude measures of life purpose, life control, will to meaning, goal seeking, future mean to fulfill, life satisfaction, and religiosity were applied. Finally, such socio-psychological measures as age, gender, marital status, income, and age-gap between couples were assumed to function as moderators. With 430 valid study samples, ages from 20s to 50s, with more females(316) than males(114), with average personal possessions of 5 genuine and 9 counterfeit luxury brands, we conducted questionnaire survey. Results indicated that social-adjustive function is totally disappeared in the relationship due to current social trend of widespread consumptions on both genuine and counterfeit brands which in turn, make consumers feel less special on wearing or carrying them unlike in the past. Self-expressive function and rationality-consumptive functions act as strong catalysts for genuine brand consumption and counterfeit brand consumption, respectively. On consumers' decision-making styles, high-price sublation is the most powerful indicator anticipating counterfeit consumption, even more powerful than personal incomes. In life attitude, the overall model fit was not validated, and only life control and life satisfaction are proven to be significant on both genuine and counterfeit product consumptions. Employment of socio-psychological factors in the model improved understanding of users further. Young consumers tend to go for genuine products over counterfeits. Consumers in different income groups; low, medium and high, all significantly consume genuine products for reasons of different decision-making styles. The results indicated that consumers whose personal disposition is predisposed to consume products in the form of reflection of his or her personality, go only for genuine brands for quality reason, while consumers who rationally consume products for its function or usability, go only for counterfeits for high-price sublation reason. Meanwhile, both product users support for high-price orientation who are not well off.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.135-141
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• 2011

Recent advances in mobile devices and development of various mobile applications dealing with private information of users made user authentication in mobile devices a very important issue. This paper presents a new user authentication method based on touch screen interfaces. This method uses for authentication the PIN digits as well as the exact locations the user touches to input these digits. Our method is fully compatible with the regular PIN entry method which uses numeric keypads, and it provides better usability than the behavioral biometric schemes because its PIN registration process is much simpler. According to our experiments, our method guarantees EERs of 12.8%, 8.3%, and 9.3% for 4-digit PINs, 6-digit PINs, and 11-digit cell phone numbers, respectively, under the extremely conservative assumption that all users have the same PIN digits and cell phone numbers. Thus we can guarantee much higher performance in identification functionality by applying this result to a more practical situation where every user uses distinct PIN and sell phone number. Finally, our method is far more secure than the regular PIN entry method, which is verified by our experiments where attackers are required to recover a PIN after observing the PIN entry processes of the regular PIN and our method under the same level of security parameters.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.2
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• pp.67-73
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• 2005

Recently, the social requirement of personal identification techniques has been increasing. Fingerprint recognition is one of the biometries methods that has been widely used for this requirement. This paper proposes the fingerprint matching algorithm that uses the information of the ridge shapes of minutiae. In which, the data of the ridge shape are expressed in one-dimensional discrete-time signals. In our algorithm, we obtain one-dimensional discrete-time signals for ridge at every minutiae from input and registered fingerprints, and find pairs of minutia which have the similar ridge shape by comparing input fingerprint with registered fingerprint, thereafter we find candidates of rotation angle and moving displacement from the pairs of similar minutia, and obtain the final rotation angle and moving displacement value from those candidates set by using clustering method. After that, we align an input fingerprint by using obtained data, and calculate the matching rate by counting the number of corresponded pairs of minutia within the overlapped area of an input and registered fingerprints. As a result of experiment, false rejection rate(FRR) of $18.0\%$ at false acceptance rate(FAR) of $0.79\%$ is achieved.