research-article

Naive Bayes Classification based on Differential Privacy

Authors:

Mingshuang LiAuthors Info & Claims

AIAM 2019: Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing

Article No.: 65, Pages 1 - 6

https://doi.org/10.1145/3358331.3358396

Published: 17 October 2019 Publication History

Abstract

Data mining has a wide range of applications in the real world. However, it is possible to disclose the private information of users in the process of data mining. Therefore, it is of great significance to protect the users' privacy while mining the knowledge behind the data. In this paper, we propose a Naive Bayes classification method based on differential privacy. For nominal attributes, we add Laplace noise to the count. For numerical attributes, we add Laplace noise to the mean, standard deviation, and scale parameter, and then use the noisy parameters to calculate the prior probability and conditional probability. For numerical attributes, we assume that they follow Gaussian, Laplace, or lognormal distribution, and apply our algorithms to compare utilities.

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Cited By

Han BShin HKim YChoi JLee Y(2024)HEaaN-NB: Non-Interactive Privacy-Preserving Naive Bayes Using CKKS for Secure Outsourced Cloud ComputingIEEE Access10.1109/ACCESS.2024.343816112(110762-110780)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3438161
Kari VAmalanathan G(2024)Targeted prevention of risky deals for improper granular data with deep learningInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02646-816:2(750-764)Online publication date: 6-Dec-2024
https://doi.org/10.1007/s13198-024-02646-8
Han BKim YChoi JShin HLee YBrenner MCostache ARohloff K(2023)Fully Homomorphic Privacy-Preserving Naive Bayes Machine Learning and ClassificationProceedings of the 11th Workshop on Encrypted Computing & Applied Homomorphic Cryptography10.1145/3605759.3625262(91-102)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3605759.3625262
Show More Cited By

Index Terms

Naive Bayes Classification based on Differential Privacy
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections

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cover image ACM Other conferences

AIAM 2019: Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing

October 2019

418 pages

ISBN:9781450372022

DOI:10.1145/3358331

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2019

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Funding Sources

the Foundation of Guizhou Provincial Key Laboratory of Public Big Data
the Fundamental Research Funds for the Central Universities

Conference

AIAM 2019

AIAM 2019: 2019 International Conference on Artificial Intelligence and Advanced Manufacturing

October 17 - 19, 2019

Dublin, Ireland

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Overall Acceptance Rate 100 of 285 submissions, 35%

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Cited By

Han BShin HKim YChoi JLee Y(2024)HEaaN-NB: Non-Interactive Privacy-Preserving Naive Bayes Using CKKS for Secure Outsourced Cloud ComputingIEEE Access10.1109/ACCESS.2024.343816112(110762-110780)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3438161
Kari VAmalanathan G(2024)Targeted prevention of risky deals for improper granular data with deep learningInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02646-816:2(750-764)Online publication date: 6-Dec-2024
https://doi.org/10.1007/s13198-024-02646-8
Han BKim YChoi JShin HLee YBrenner MCostache ARohloff K(2023)Fully Homomorphic Privacy-Preserving Naive Bayes Machine Learning and ClassificationProceedings of the 11th Workshop on Encrypted Computing & Applied Homomorphic Cryptography10.1145/3605759.3625262(91-102)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3605759.3625262
Hewage USinha RNaeem M(2023)Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature reviewArtificial Intelligence Review10.1007/s10462-023-10425-356:9(10427-10464)Online publication date: 22-Feb-2023
https://doi.org/10.1007/s10462-023-10425-3
Ghosh ASenthilrajan A(2022)A Modified Naïve Bayes Classifier for Detecting Spam E-mails based on Feature Selection2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS)10.1109/ICICCS53718.2022.9788340(1634-1641)Online publication date: 25-May-2022
https://doi.org/10.1109/ICICCS53718.2022.9788340
Zhao RWen XPang HMa Z(2021)Liver disease prediction using W-LR-XGB Algorithm2021 International Conference on Computer, Blockchain and Financial Development (CBFD)10.1109/CBFD52659.2021.00055(245-248)Online publication date: Apr-2021
https://doi.org/10.1109/CBFD52659.2021.00055
Tang WZhou YLi MLu L(2020)Differential Privacy Preserving Naive Bayes Classification via Wavelet Transform2020 International Conference on Networking and Network Applications (NaNA)10.1109/NaNA51271.2020.00021(81-85)Online publication date: Dec-2020
https://doi.org/10.1109/NaNA51271.2020.00021

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