Bayesian Networks-based personal data synthesis

Deeva, Irina; Andriushchenko, Petr; Kalyuzhnaya, Anna V.; Boukhanovsky, Alexander V.

doi:10.1145/3411170.3411243

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…In this connection, the authors proposed to use a common formal basis for the analysis and comparison of communication protocols. Determining the requirements for the security of personal data for the entire life cycle of technologies is a key requirement [5], and the Bayesian methodology [24] and the method differentiated confidentiality [25] were studied for the analysis of personal data in the social network [26], [27].…”

Section: Related Workmentioning

confidence: 99%

The Bayes model for the protection of human interests

Zharova,

Elin,

Levashov

2023

IJECE

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<span lang="EN-US">This article is aimed at solving a number of issues related to the problems, risks, and threats arising from the profiling of human activity. In this study, the Bayesian method was used, to determine the quantitative and qualitative characteristics of personal data for ensuring the security of this data by dint of reducing the redundancy of data processed by artificial intelligence (AI). A thought experiment to test the possibility of reducing the redundancy of personal data processed by AI allows us to conclude that using the Bayesian method allows to protect human rights to privacy. With this approach, instead of the method associated with the collection and accumulation of the most sensitive categories of personal data, we proposed a method that is associated with obtaining probabilistic estimates of the values of the parameters of these data by conducting statistical studies of the specified personal data without their collection and accumulation. The probabilistic estimates of the parameters of some sensitive personal data obtained in this way can replace their exact values and can be used by AI in the criteria for filtering personal data subjects, including for the purpose of making a decision.</span>

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Section: Related Workmentioning

confidence: 99%

The Bayes model for the protection of human interests

Zharova,

Elin,

Levashov

2023

IJECE

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A Multimodal Approach to Synthetic Personal Data Generation with Mixed Modelling: Bayesian Networks, GAN’s and Classification Models

Deeva

Mossyayev

Kalyuzhnaya

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Synthetic Generation of Trip Data: The Case of Smart Card

Kieu,

Meredith,

Raith

2023

Data Sci. Transp.

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While individual data are key for epidemiology, social simulation, economics, and various other fields, data owners are increasingly required to protect the personally identifiable information from data. Simple data de-identification or ‘data masking’ measures are limited, because they both reduce the utility of the dataset and are not sufficient to protect individual confidentiality. This paper provides detail on the creation of a synthetic trip data in transportation, with the Smart Card data as the case study. It discusses and compares two machine learning methods, a Generative Adversarial Network and a Bayesian Network for modelling and generating this dataset. The synthetic data retain important utility of the real dataset, e.g., the origin, destination, and time of travel, while each data point does not represent a real trip in the original dataset. The synthetic dataset can be used in various applications, including microsimulation of public transport systems, analysing travel behaviours, model predictive control of transit flows, or evaluation of transport policies.

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Bayesian Networks-based personal data synthesis

Cited by 4 publications

References 16 publications

The Bayes model for the protection of human interests

The Bayes model for the protection of human interests

A Multimodal Approach to Synthetic Personal Data Generation with Mixed Modelling: Bayesian Networks, GAN’s and Classification Models

Synthetic Generation of Trip Data: The Case of Smart Card

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