ACM International Conference Proceeding Series, pages 6-11
Bayesian Networks-based personal data synthesis
Publication type: Proceedings Article
Publication date: 2020-09-14
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Abstract
Often, confidentiality problems and a lack of original data, make it challenging to analyze user data carefully. In such situations, synthetic data can be used that is more suitable for testing and training marketing strategies, personalized assistants, or behavior analysis systems than the original data. In this paper, the approach for generating synthetic social media profiles data based on Bayesian networks was analyzed. The personal data synthesis problem was considered as the inference of a joint probability distribution from the oriented probabilistic models like Bayesian networks. The quality of this approach in generating VKontakte (VK is the Russian analog of Facebook) social network data was demonstrated and assessed. The Bayesian network approach has shown itself well in the tasks of deriving joint and marginal data distributions, which has led to the production of high-quality synthetic personal data.
Citations by journals
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Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
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Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
1 publication, 50%
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Data Science for Transportation
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Data Science for Transportation
1 publication, 50%
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1
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Citations by publishers
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1
2
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Springer Nature
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Springer Nature
2 publications, 100%
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Deeva I. et al. Bayesian Networks-based personal data synthesis // ACM International Conference Proceeding Series. 2020. pp. 6-11.
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Deeva I., Andriushchenko P. D., Kalyuzhnaya A. V., Boukhanovsky A. V. Bayesian Networks-based personal data synthesis // ACM International Conference Proceeding Series. 2020. pp. 6-11.
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TY - CPAPER
DO - 10.1145/3411170.3411243
UR - https://doi.org/10.1145%2F3411170.3411243
TI - Bayesian Networks-based personal data synthesis
T2 - ACM International Conference Proceeding Series
AU - Deeva, Irina
AU - Andriushchenko, Petr D
AU - Kalyuzhnaya, Anna V
AU - Boukhanovsky, Alexander V
PY - 2020
DA - 2020/09/14 00:00:00
SP - 6-11
ER -
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@inproceedings{2020_Deeva,
author = {Irina Deeva and Petr D Andriushchenko and Anna V Kalyuzhnaya and Alexander V Boukhanovsky},
title = {Bayesian Networks-based personal data synthesis},
year = {2020},
pages = {6--11},
month = {sep}
}
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