• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.165-184
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• 2023

Recently, Deep learning analysis of unstructured text data using language models, such as Google's BERT and OpenAI's GPT has shown remarkable results in various applications. Most language models are used to learn generalized linguistic information from pre-training data and then update their weights for downstream tasks through a fine-tuning process. However, some concerns have been raised that privacy may be violated in the process of using these language models, i.e., data privacy may be violated when data owner provides large amounts of data to the model owner to perform fine-tuning of the language model. Conversely, when the model owner discloses the entire model to the data owner, the structure and weights of the model are disclosed, which may violate the privacy of the model. The concept of offsite tuning has been recently proposed to perform fine-tuning of language models while protecting privacy in such situations. But the study has a limitation that it does not provide a concrete way to apply the proposed methodology to text classification models. In this study, we propose a concrete method to apply offsite tuning with an additional classifier to protect the privacy of the model and data when performing multi-classification fine-tuning on Korean documents. To evaluate the performance of the proposed methodology, we conducted experiments on about 200,000 Korean documents from five major fields, ICT, electrical, electronic, mechanical, and medical, provided by AIHub, and found that the proposed plug-in model outperforms the zero-shot model and the offsite model in terms of classification accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.5
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• pp.149-160
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• 2008

As individual users have to provide more information than the minimum for using information communication service, the invasion of privacy of Individual users is increasing. That is why client/server based personal information security platform technologies are being developed such as P3P, EPAL and XACML. By the way enterprises and organizations using primarily role based access control can not use these technologies. because those technologies apply access control policies to individual subjects. In this paper, we suggest an expression language for privacy enhancing role-based access control policy. Suggested privacy enhancing role-based access control policy language model is a variation of XACML which uses matching method and condition, and separately contains elements of role, purpose, and obligation. We suggest policy language model for permission assignment in this paper, shows not only privacy policy scenario with policy document instance, but also request context and response context for helping understanding.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.53-63
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• 2011

An SNS(Social Network Service) enables people to form a social network on online as in the real world. With the rising popularity of the service, side effects of SNSs were issued. Therefore we propose and implement a policy-based privacy protection module and access control policy language for ensuring the right of control of personal information and sharing data among SNSs. The policy language for protecting privacy is based on an attribute-based access control model which grants an access to personal information based on a user's attributes. The policy language and the privacy protection module proposed to give the right of control of personal information to the owner, they can be adopted to other application domains in which privacy protection is needed as well as secure sharing data among SNSs.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.4
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• pp.135-144
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• 2010

Privacy protection can only be achieved by enforcing privacy policies within an enterprise's on and offline data processing systems. There are P-RBAC model and purpose based model and obligations model among privacy policy models. But only these models each can not dynamically deal with the rapidly changing business environment. Even though users are in the same role, on occasion, secure system has to opt for a figure among them who is smart, capable and supremely confident and to give him/her a special mission during a given period and to strengthen privacy protection by permitting to present fluently access control conditions. For this, we propose Integrated Privacy Protection Model based on RBAC. Our model includes purpose model and P-RBAC and obligation model. And lastly, we define high level policy language model based XML to be independent of platforms and applications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.3 no.4
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• pp.319-324
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• 2002

The Integration of agent technology and security technology is needed to many application areas like electronic commerce. This paper suggests a model of extended multi-agent engine which supports privacy, integrity, authentication and non-repudiation on agent communication. Each agent which is developed with the agent engine is composed of agent engine layer and agent application layer. We describe and use the concepts self-to-self messages, secure communication channel, and distinction of KQML messages in agent application layer and messages in agent engine layer. The suggested agent engine provides an agent communication language which is extended to enable secure communication between agents without any modifications or restrictions to content layer and message layer of KQML. Also, in the model of our multi-agent engine, secure communication is expressed and processed transparently on the agent communication language.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.3 no.4
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• pp.313-318
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• 2002

The Integration of agent technology and security technology is needed to many application areas like electronic commerce. This paper suggests a model of extended multi-agent engine which supports privacy, integrity, authentication and non-repudiation on agent communication. Each agent which is developed with the agent engine is composed of agent engine layer and agent application layer. We describe and use the concepts self-to-self messages, secure communication channel, and distinction of KQML messages in agent application layer and messages in agent engine layer. The suggested agent engine provides an agent communication language which is extended to enable secure communication between agents without any modifications or restrictions to content layer and message layer of KQML. Also, in the model of our multi-agent engine, secure communication is expressed and processed transparently on the agent communication language.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.139-149
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• 2009

In order to verify that a lot of legal requirements and regulations are correctly translated into software, this paper provides a solution for formal and computable representations of rules and requirements in data protection legislations with a DSML (Domain Specific Modeling Language). All policies are formally specified through Prolog and then integrated with DSML, According to the time of policy verification, this solution has two kinds of policies: static policies, dynamic policies.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.56-61
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• 2023

The present study explored a comprehensive investigation of university professors' perspectives on the implementation of ChatGPT - an artificial intelligence-powered language model - in their teaching practices. A diverse group of 30 university professors responded to a questionnaire about the level of their interest in implementing the tool, willingness to apply it, and concerns they have regarding the intervention of ChatGPT in higher education setting. The results showed that the participants are highly interested in employing the tool into their teaching practice, and find that the students are likely to benefit from using ChatGPT in classroom settings. On the other hand, they displayed concerns regarding high depandency on data, privacy-related issues, lack of supports required, and technical contraints. In today's fast-paced society, educators are urged to mindfully apply this inevitable generative AI means with thoughtfulness and ethical considerations to and for their learners. Relevant topics are discussed to successfully intervene AI tools in teaching practices in higher education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1501-1509
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• 2011

Current identity management 1.0 model, which is service provider-centric and isolated, has several problems such as low usability, high cost structure, difficulty in privacy protection, and lack of trust infrastructure. Though various SSO-based identity management 2.0 models including Passport/Live ID, Liberty Alliance/SAML, CardSpace, and OpenID have been recently developed in order to overcome those problems, they are not widely accepted in real Internet environment so as to replace the existing identity management 1.0 model. This paper firstly analyzes the widely-known identity 2.0 models in a comparative way, and then presents a perspective on the development direction of identity management 3.0 model for future Internet.

• Korean Journal of Radiology
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• v.25 no.3
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• pp.224-242
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• 2024

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.