References
- 정진명, 김남규 (2023). 프라이버시 보호를 위한 오프사이트 튜닝 기반 언어모델 미세 조정 방법론. 지능정보연구, 29(4), 165-184. https://doi.org/10.13088/JIIS.2023.29.4.165
- 유기동 (2021). 연관지식의 효율적인 표현 및 추론이 가능한 지식그래프 기반 지식지도. 지능정보연구, 27(4), 49-71. https://doi.org/10.13088/JIIS.2021.27.4.049
- Agrawal, G., Kumarage, T., Alghami, Z., & Liu, H. (2023). Can knowledge graphs reduce hallucinations in LLMs? A survey. arXiv preprint arXiv:2311.07914.
- Baek, J., Aji, A. F., & Saffari, A. (2023). Knowledgeaugmented language model prompting for zero-shot knowledge graph question answering. arXiv preprint arXiv:2306.04136.
- Feng, Q., He, D., Liu, Z., Wang, H., & Choo, K. K. R. (2020). SecureNLP: A system for multi-party privacy-preserving natural language processing. IEEE Transactions on Information Forensics and Security, 15, 3709-3721. https://doi.org/10.1109/TIFS.2020.2997134
- Hao, S., Tan, B., Tang, K., Ni, B., Shao, X., Zhang, H., ... & Hu, Z. (2022). BertNet: Harvesting knowledge graphs with arbitrary relations from pretrained language models. arXiv preprint arXiv:2206.14268.
- Li, C. Y., Liang, X., Hu, Z., & Xing, E. P. (2019). Knowledge-driven encode, retrieve, paraphrase for medical image report generation. In Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 6666-6673.
- Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50-60.
- Lin, G., Hua, W., & Zhang, Y. (2024). Prompt crypt: Prompt encryption for secure communication with large language models. arXiv preprint arXiv:2402.05868.
- Mothersbaugh, D. L., Foxx, W. K., Beatty, S. E., & Wang, S. (2012). Disclosure Antecedents in an Online Service Context: The Role of Sensitivity of Information. Journal of Service Research, 15(1), 76-98. https://doi.org/10.1177/1094670511424924
- Romero, O. J., Zimmerman, J., Steinfeld, A., & Tomasic, A. (2023). Synergistic integration of large language models and cognitive architectures for robust ai: An exploratory analysis. In Proceedings of the AAAI Symposium Series, 2(1), 396-405.
- Santos, A., Colaco, A. R., Nielsen, A. B., Niu, L., Strauss, M., Geyer, P. E., Coscia, F., Albrechtsen, N. J. W., Mundt, F., Jensen, L. J. & Mann, M. (2022). A knowledge graph to interpret clinical proteomics data. Nature Biotechnology, 40(5), 692-702. https://doi.org/10.1038/s41587-021-01145-6
- Sun, Y., Wang, S., Feng, S., Ding, S., Pang, C., Shang, J., ... & Wang, H. (2021). Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation. arXiv preprint arXiv:2107.02137.
- Wang, H., & Shu, K. (2023). Explainable claim verification via knowledge-grounded reasoning with large language models. arXiv preprint arXiv:2310.05253.
- Wang, H., Zhang, F., Zhao, M., Li, W., Xie, X., & Guo, M. (2019). Multi-task feature learning for knowledge graph enhanced recommendation. In Proceedings of the World Wide Web Conference, 2000-2010.
- Xiong, C., Power, R., & Callan, J. (2017). Explicit semantic ranking for academic search via knowledge graph embedding. In Proceedings of the 26th International Conference on World Wide Web, 1271-1279.
- Yang, Y., Cao, Z., Zhao, P., Zeng, D. D., Zhang, Q., & Luo, Y. (2021). Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study. Journal of Safety Science and Resilience, 2(3), 146-156. https://doi.org/10.1016/j.jnlssr.2021.08.002
- Yao, L., Mao, C., & Luo, Y. (2019). KG-BERT: BERT for knowledge graph completion. arXiv preprint arXiv:1909.03193.
- Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024), A survey on large language model (LLM) security and privacy: The good, the bad, and the ugly. High-Confidence Computing, 4(2), 100211.
- Zafar, A., Parthasarathy, V. B., Van, C. L., Shahid, S., & Shahid, A. (2023). Building trust in conversational ai: A comprehensive review and solution architecture for explainable, privacy-aware systems using llms and knowledge graph. arXiv preprint arXiv:2308.13534.
- Zhao, C., Zhao, S., Zhao, M., Chen, Z., Gao, C. Z., Li, H., & Tan, Y. A. (2019). Secure multi-party computation: Theory, practice and applications. Information Sciences, 476, 357-372. https://doi.org/10.1016/j.ins.2018.10.024
- Zhao, J., Chen, Y., & Zhang, W. (2019). Differential privacy preservation in deep learning: Challenges, opportunities and solutions. IEEE Access, 7, 48901-48911. https://doi.org/10.1109/ACCESS.2019.2909559
- Zhu, K., Wang, J., Zhou, J., Wang, Z., Chen, H., Wang, Y., Yang, L., Ye, W., Gong, N. Z., Zhang, Y., & Xie, X. (2023). Promptbench: Towards evaluating the robustness of large language models on adversarial prompts. arXiv preprint arXiv:2306.04528. [URL]
- Bratanic, T. (2023). Knowledge graph-based chatbot with GPT-3 and Neo4j. Medium, https://medium.com/neo4j/knowledge-graph-based-chatbot-with-gpt-3-and-neo4j-c4ebbd325ed
- Cuomo, J. (2023). Exploring the risks and alternatives of ChatGPT: Paving a path to trustworthy AI. IBM, https://www.ibm.com/blog/exploring-the-risks-and-alternatives-of-chatgpt-paving-a-path-to-trustworthy-ai/
- Huang, S. (2022). English to Cypher with GPT-3 in Doctor.ai. Medium, https://towardsdatascience.com/gpt-3-for-doctor-ai-1396d1cd6fa5
- Lim, S. B. (2023). 7 biggest ChatGPT security risks for organisations. https://metomic.io/resource-centre/is-chatgpt-a-security-risk-to-your-business
- OpenAI (2024). What is ChatGPT? Commonly asked questions about ChatGPT. https://help.openai.com/en/articles/6783457-what-is-chatgpt
- Singhal, A. (2012). Introducing the Knowledge Graph: things, not strings. https://blog.google/products/search/introducing-knowledge-graph-things-not/