• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3577-3588
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• 1999

This paper describes design and implementation of a general purpose cyber educational system. In this system, there are different types of menus and services according to such user's rights as system operators, professors, assistant instructors, students. In order to keep in close contact between students and professors, provided are various services such as the official announcement, transcripts of lecture, bulletin board, Q/A corner, report box, archive of study materials, electronic mail box, memorandum including checking of one's attendance and school record. For the purpose of activating small group studies and discussion about given subject, it can organize into groups automatically and provide with debating rooms. The system is implemented by separating with both HTML design and programming part by templete concept. Overload of iterative server is solved by adjusting the numbers of DB servers of each service.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.28-35
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• 2000

In order to acquire the precise and fast response for an analytical query, proper selection of the views to materialize in data warehouse is very crucial. In traditional algorithms, the whole relation is considered to be selected as materialized views. However, materializing the whole relation rather than a part of relation results in much worse performance in terms of time and space cost. Therefore, we present a new algorithm for selection of views to materialize using clustering method in order to improve the performance of data warehouse including this problem. In the presented algorithm, ASVMR(Algorithm for Selection of Views to Materialize using Reduced table), we first generate reduced tables in data warehouse using automatic clustering based on attribute-values density, then we consider the combination of reduced tables as materialized views instead of the combination of the original base relations. We also show the experimental results in which both time and space cost are approximately 1.8 times better than the conventional algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.451-461
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• 2024

Recently, various AI chatbots based on large language models have been released. Chatbots have the advantage of providing users with quick and easy information through interactive prompts, making them useful in various fields such as question answering, writing, and programming. However, a vulnerability in chatbots called "prompt injection attacks" has been proposed. This attack involves injecting instructions into the chatbot to violate predefined guidelines. Such attacks can be critical as they may lead to the leakage of confidential information within large language models or trigger other malicious activities. However, the vulnerability of Korean prompts has not been adequately validated. Therefore, in this paper, we aim to generate malicious Korean prompts and perform attacks on the popular chatbot to analyze their feasibility. To achieve this, we propose a system that automatically generates malicious Korean prompts by analyzing existing prompt injection attacks. Specifically, we focus on generating malicious prompts that induce harmful expressions from large language models and validate their effectiveness in practice.