Computed tomography image reconstruction using stacked U-Net.

Since the development of deep learning methods, many researchers have focused on image quality improvement using convolutional neural networks. They proved its effectivity in noise reduction, single-image super-resolution, and segmentation. In this study, we apply stacked U-Net, a deep learning method, for X-ray computed tomography image reconstruction to generate high-quality images in a short time with a small number of projections.

Privacy-preserving chi-squared test of independence for small samples.

Background: The importance of privacy protection in analyses of personal data, such as genome-wide association studies (GWAS), has grown in recent years. GWAS focuses on identifying single-nucleotide polymorphisms (SNPs) associated with certain diseases such as cancer and diabetes, and the chi-squared (χ) hypothesis test of independence can be utilized for this identification. However, recent studies have shown that publishing the results of χ tests of SNPs or personal data could lead to privacy violations.

Differentially Private Mobile Crowd Sensing Considering Sensing Errors.

An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people's surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable research has been conducted over the last decade to develop participatory sensing that looks to preserve privacy while analyzing participants' surrounding information. To protect privacy, each participant perturbs the sensed data in his or her device, then the perturbed data is reported to the data collector.

Privacy-preserving Chi-squared testing for genome SNP databases.

In recent years, the importance of privacy protection in genome-wide association studies (GWAS) has been increasing. GWAS focuses on identifying single-nucleotide polymorphisms (SNPs) associated with certain diseases such as cancer and diabetes, and Chi-squared testing can be used for this. However, recent studies reported that publishing the p-value or the corresponding chi-squared value of analyzed SNPs can cause privacy leakage.