@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00223096,
 author = {Hernandez-Matamoros, Andres and Kikuchi, Hiroaki and Andres, Hernandez-Matamoros and Hiroaki, Kikuchi},
 book = {コンピュータセキュリティシンポジウム2022論文集},
 month = {Oct},
 note = {When we use a service which has an internet connection or not, the concern of how we can protect our data appears. Besides, the company that offers services wants to use our information to improve the service. Local Differential Privacy is applied to satisfy users' and companies' requirements about privacy concerns. This paper shows a risk Evaluation of LoPub, an LDP scheme. We show the results when the Central Server uses VAE instead uses the LoPub proposal. The results show that only a single VAE model performs well in a dataset with low cardinality on the attributes. Furthermore, it clears the way to continue researching VAE in LDP so that VAE can be applied to high cardinality datasets., When we use a service which has an internet connection or not, the concern of how we can protect our data appears. Besides, the company that offers services wants to use our information to improve the service. Local Differential Privacy is applied to satisfy users' and companies' requirements about privacy concerns. This paper shows a risk Evaluation of LoPub, an LDP scheme. We show the results when the Central Server uses VAE instead uses the LoPub proposal. The results show that only a single VAE model performs well in a dataset with low cardinality on the attributes. Furthermore, it clears the way to continue researching VAE in LDP so that VAE can be applied to high cardinality datasets.},
 pages = {289--296},
 publisher = {情報処理学会},
 title = {Risk Evaluation of LDP scheme LoPub against Variational Autoencoder},
 year = {2022}
}