• Title/Summary/Keyword: 재식별 위험 측정

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A Study on Measuring the Risk of Re-identification of Personal Information in Conversational Text Data using AI

  • Dong-Hyun Kim;Ye-Seul Cho;Tae-Jong Kim
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.10
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    • pp.77-87
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    • 2024
  • With the recent advancements in artificial intelligence, various chatbots have emerged, efficiently performing everyday tasks such as hotel bookings, news updates, and legal consultations. Particularly, generative chatbots like ChatGPT are expanding their applicability by generating original content in fields such as education, research, and the arts. However, the training of these AI chatbots requires large volumes of conversational text data, such as customer service records, which has led to privacy infringement cases domestically and internationally due to the use of unrefined data. This study proposes a methodology to quantitatively assess the re-identification risk of personal information contained in conversational text data used for training AI chatbots. To validate the proposed methodology, we conducted a case study using synthetic conversational data and carried out a survey with 220 external experts, confirming the significance of the proposed approach.

Design on Supporting Tool of Process Capability Metric for Effectiveness Process Management (효과적인 프로세스 관리를 위한 PCM(Process Capability Metric) 지원 도구 설계)

  • Yeom, Hee-Gyun;Jung, Il-Jae;Chae, Hynn-Choul;Hwang, Sun-Myung
    • Annual Conference of KIPS
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    • 2007.05a
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    • pp.267-270
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    • 2007
  • 효과적인 소프트웨어 프로세스 개선을 위해 SPICE와 CMMI 프로세스 심사 표준을 도입하려는 노력을 하고 있다. 이러한 표준을 통해 효과적인 개선하기 위해서는 개선점과 위험을 식별하고 이들 이슈들을 개발환경에 적용시켜서 조직의 비전에 대응한 작업성능을 높여야한다. 지속적인 개선을 필요로 하는 조직은 현재의 작업성능을 측정하고 이를 개선하기 위한 개선점을 찾아내는 능력과 경험을 축적하여 체계적으로 관리하는 것이 중요하다. 하지만 기존의 SPI 모델들은 무엇을 수행해야 하는지에 대한 지침은 제공하고 있지만, 정량적인 작업성능 측정 및 특정 환경의 소프트웨어 개발 조직의 SPI를 위해 필요한 구체적인 지침을 제시하고 있지는 않다. 따라서, 본 논문에서는 정량적인 SPI룰 위해 프로세스 측정 메트릭 정의와 심사 경험이 분석되어 활용될 수 있는 PCM(Process Capability Metric) Experience Factory 모델을 제안한다.

Locates the Sunken Ship 'Dmitri Donskoi' using Marine Geophysical Survey Techniques in Deep Water (지구물리 탐사기법을 이용한 심해 Dmitri Donskoi호 확인)

  • Yoo, Hai-Soo;Kim, Su-Jeong;Park, Dong-Won
    • 한국지구물리탐사학회:학술대회논문집
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    • 2004.08a
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    • pp.104-117
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    • 2004
  • Dmitri Donskoi, which went down during the Russo-Japanese War occurred 100 years ago, was found by using geophysical exploration techniques at the 400 m water depth of submarine valley off Jeodong of Ulleung Island. In the submarine area with the rugged seabed topography and volcanic seamounts, in particular, the reliable seabed images were acquired by using the mid-to-shallow Multibeam exploration technique The strength of corrosion (causticity) of the sunken Donskoi, measured by the electrochemical method, decreased to 2/5 compared with the original strength.

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