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Kangwon National University

Academic institutionasia · kr
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Research library13linked papers
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Selected work

Representative Papers

MIFA: An MILP-based Framework for Improving Differential Fault Attacks

Aug 07, 2026

This work addresses the inefficiency of differential fault analysis (DFA) by proposing the first DFA framework based on mixed-integer linear programming (MILP). For the first time, MILP is employed to systematically search for differential trails with a unique solution, combined with bit-level single-bit flip fault modeling to optimize both the location and number of injected faults. The approach enables attacks on deeper-round implementations and allows theoretical computation of the minimal number of faults required to recover the secret key. When applied to the DEFAULT block cipher, the method uniquely recovers the full key with only three faults in the sixth-to-last round and two faults each in the seventh- and eighth-to-last rounds, significantly outperforming existing DFA results and effectively breaking the cipher’s claimed DFA resistance.

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Leveraging External Knowledge for Historical Document Restoration via Retrieval-Augmented Large Language Models

Jul 23, 2026

Historical documents are often rendered partially illegible due to physical degradation, posing significant challenges—particularly in recovering proper nouns that rely heavily on external contextual knowledge. This work proposes a novel framework that integrates implicit knowledge from large language models with explicit historical context retrieved from external knowledge bases, leveraging retrieval-augmented generation (RAG) for the joint restoration of both general characters and named entities. By incorporating context-aware reasoning to effectively fuse domain-specific knowledge, the method substantially outperforms existing baselines on Korean historical documents, achieving marked improvements in both character-level and named entity recovery accuracy. The approach has also been endorsed by domain experts as a practical tool for historical text analysis.

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Recent publications

Latest Papers

MIFA: An MILP-based Framework for Improving Differential Fault Attacks

Aug 07, 2026

This work addresses the inefficiency of differential fault analysis (DFA) by proposing the first DFA framework based on mixed-integer linear programming (MILP). For the first time, MILP is employed to systematically search for differential trails with a unique solution, combined with bit-level single-bit flip fault modeling to optimize both the location and number of injected faults. The approach enables attacks on deeper-round implementations and allows theoretical computation of the minimal number of faults required to recover the secret key. When applied to the DEFAULT block cipher, the method uniquely recovers the full key with only three faults in the sixth-to-last round and two faults each in the seventh- and eighth-to-last rounds, significantly outperforming existing DFA results and effectively breaking the cipher’s claimed DFA resistance.

0 citationsRead paper

Leveraging External Knowledge for Historical Document Restoration via Retrieval-Augmented Large Language Models

Jul 23, 2026

Historical documents are often rendered partially illegible due to physical degradation, posing significant challenges—particularly in recovering proper nouns that rely heavily on external contextual knowledge. This work proposes a novel framework that integrates implicit knowledge from large language models with explicit historical context retrieved from external knowledge bases, leveraging retrieval-augmented generation (RAG) for the joint restoration of both general characters and named entities. By incorporating context-aware reasoning to effectively fuse domain-specific knowledge, the method substantially outperforms existing baselines on Korean historical documents, achieving marked improvements in both character-level and named entity recovery accuracy. The approach has also been endorsed by domain experts as a practical tool for historical text analysis.

0 citationsRead paper