An Efficient Solution for Privacy-preserving Naïve Bayes Classification in Fully Distributed Data Model

Authors

  • Vu Duy Hien

DOI:

https://doi.org/10.54654/isj.v1i15.840

Keywords:

privacy-preserving data mining and machine learning, secure multi-party computation, Naïve Bayes classification, Homomorphic encryption, Data privacy

Tóm tắt

AbstractRecently, privacy preservation has
become one of the most important problems in
data mining and machine learning. In this paper,
we propose a novel privacy-preserving Naïve
Bayes classifier for the fully distributed data
scenario where each record is only kept by a
unique owner. Our proposed solution is based on
a secure multi-party computation protocol, so that
it has the capability to securely protect each data
owner’s privacy, as well as accurately guarantee
the classification model. Furthermore, our
experimental results show that the new solution is
efficient enough for practical applications.
Tóm tắtGần đây, bảo vệ tính riêng tư đã trở
thành một trong những vấn đề quan trọng nhất
trong khai phá dữ liệu và học máy. Trong bài báo
này, chúng tôi đề xuất một bộ phân lớp Naïve
Bayes đảm bảo tính riêng tư mới cho kịch bản dữ
liệu phân tán đầy đủ trong đó mỗi bản ghi chỉ
được giữ bởi một người sở hữu duy nhất. Giải
pháp nhóm tác giả đề xuất được dựa trên tính
toán bảo mật nhiều thành viên nên nó có khả năng
bảo vệ an toàn sự riêng tư của mỗi người sở hữu
dữ liệu cũng như đảm bảo tính chính xác của mô
hình phân lớp. Hơn thế nữa, các kết quả thực
nghiệm của chúng tôi chỉ ra rằng giải pháp mới
đủ hiệu quả trong các ứng dụng thực tế.

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Published

2022-06-08

How to Cite

Hien, V. D. (2022). An Efficient Solution for Privacy-preserving Naïve Bayes Classification in Fully Distributed Data Model. Journal of Science and Technology on Information Security, 1(15), 56-61. https://doi.org/10.54654/isj.v1i15.840

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