Secure naïve bayes classification protocol over encrypted data using fully homomorphic encryption

Yoshiko Yasumura, Yu Ishimaki, Hayato Yamana

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine learning classification has a wide range of applications. In the big data era, a client may want to outsource classification tasks to reduce the computational burden at the client. Meanwhile, an entity may want to provide a classification model and classification services to such clients. However, applications such as medical diagnosis require sensitive data that both parties may not want to reveal. Fully homomorphic encryption (FHE) enables secure computation over encrypted data without decryption. By applying FHE, classification can be outsourced to a cloud without revealing any data. However, existing studies on classification over FHE do not achieve the scenario of outsourcing classification to a cloud while preserving the privacy of the classification model, client's data and result. In this work, we apply FHE to a naïve Bayes classifier and, to the best of our knowledge, propose the first concrete secure classification protocol that satisfies the above scenario.

Original languageEnglish
Title of host publication21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Proceedings
EditorsMaria Indrawan-Santiago, Eric Pardede, Ivan Luiz Salvadori, Matthias Steinbauer, Ismail Khalil, Gabriele Anderst-Kotsis
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450371797
DOIs
Publication statusPublished - 2019 Dec 2
Event21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Munich, Germany
Duration: 2019 Dec 22019 Dec 4

Publication series

NameACM International Conference Proceeding Series

Conference

Conference21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019
CountryGermany
CityMunich
Period19/12/219/12/4

Keywords

  • Classification
  • Data privacy
  • Fully homomorphic encryption
  • Machine learning
  • Privacy-preservation

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Yasumura, Y., Ishimaki, Y., & Yamana, H. (2019). Secure naïve bayes classification protocol over encrypted data using fully homomorphic encryption. In M. Indrawan-Santiago, E. Pardede, I. L. Salvadori, M. Steinbauer, I. Khalil, & G. Anderst-Kotsis (Eds.), 21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Proceedings (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3366030.3366056