Researcher Profile and Settings

Affiliation

• ＜Faculty / Graduate School / Others＞

  Graduate School of Engineering / Department of Electrical and Electronic Engineering

• ＜Related Faculty / Graduate School / Others＞

  Faculty of Engineering / Department of Electrical and Electronic Engineering

Degree

• Ph.D.

Teaching

• Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Computer Programming Practice IIA

  Link to Kobe Univ. syllabus
• Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Computer Programming Practice Ⅱ

  Link to Kobe Univ. syllabus
• Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Computer Programming Practice IIB

  Link to Kobe Univ. syllabus
• Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electrical and Electronics Engineering Laboratory ⅢA

  Link to Kobe Univ. syllabus
• Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electrical and Electronics Engineering Laboratory ⅢB

  Link to Kobe Univ. syllabus

Research Activities

Published Papers

• Privacy-Preserving Naive Bayes Classification Using Fully Homomorphic Encryption.

  Sangwook Kim, Masahiro Omori, Takuya Hayashi 0001, Toshiaki Omori, Lihua Wang 0001, Seiichi Ozawa

  Many services for data analysis require customer’s data to be exposed and privacy issues are critical in related fields. To address this problem, we propose a Privacy-Preserving Naive Bayes classifier (PP-NBC) model which provides classification results without leaking privacy information in data sources. Through classification process in PP-NBC, the operations are evaluated us

  Springer, 2018, Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science, 11304, 349 - 358, English

  [Refereed]

  International conference proceedings

Presentations

• Privacy-Preserving naive Bayes Classification using Fully Homomorphic Encryption

  Sangwook Kim, Masahiro Omori, Takuya Hayashi, Toshiaki Omori, Lihua Wang, Seiichi Ozawa

  The 25th International Conference on Neural Information Processing, Dec. 2018, English, Many services for data analysis require customer’s data to be exposed and privacy issues are critical in related fields. To address this problem, we propose a Privacy-Preserving Naive Bayes classifier (PP-NBC) model which provides classification results without leaking privacy information in data sources. Through classification process in PP-NBC, the operations are evaluated using, International conference

  Oral presentation

• Privacy-Preserving Naive Bayes Classifier based on Homomorphic Encryption

  KIM Sangwook, OMORI Toshiaki, OMORI Masahiro, HAYASHI Takuya, WANG Lihua, OZAWA Seiichi

  The 13th International Workshop on Security (IWSEC2018), Sep. 2018, English, IWSEC, Sakura Hall, Tohoku University (仙台市), International conference

  Poster presentation

• Detection of JavaScript-based Attacks Using Doc2Vec Feature Learning

  NIDCHU Samuel, KIM Sangwook, OZAWA Seiichi, MISU Takeshi, MAKISHIMA Kazuo

  The 13th International Workshop on Security (IWSEC2018), Sep. 2018, English, IWSEC, Sakura Hall, Tohoku University (仙台市), Websites attracts millions of visitors due to the convenience of services they offer. These include news, entertainment and educational contents among many others. However, a large number of users frequenting these sites are considered as interesting targets for cyber attackers. These websites are injected with malicious codes by exploiting vulnerabilities in servers, plugins a, International conference

  Poster presentation