Chaminade College Preparatory School

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author.Additional informationNotes on contributorsRobert P. BarnidgeRobert P. Barnidge, Jr., is a social studies teacher at Chaminade College Preparatory School, St. Louis, Missouri. He is also an adjunct instructor at the University of Missouri—St. Louis.

Recently, machine learning has been frequently applied in multiple areas, like image classification and object recognition. However, some machine learning applications train their models on sensitive data that could leak personal information, leading to potential privacy issues. Many privacy attacks against deep learning models have been proposed. A membership inference attack was demonstrated to be a type of effective attack and a real threat to privacy in machine learning. Here, we trained three different convolutional neural networks for image classification with the CIFAR10 dataset and examined the effects of a membership inference attack on these models. We also explore different defense strategies, including differential privacy against membership inference attack. We showed that membership inference attack was more effective against overfitting models and defense strategies like differential privacy and regularization can significantly lower the attack accuracy.

Pore size, external shape, and internal complexity of additively manufactured porous titanium scaffolds are three primary determinants of cell viability and structural strength of scaffolds in bone tissue engineering. To obtain an optimal design with the combination of all three determinants, four scaffolds each with a unique topology (external geometry and internal structure) were designed and varied the pore sizes of each scaffold 3 times. For each topology, scaffolds with pore sizes of 300, 400, and 500 µm were designed. All designed scaffolds were additively manufactured in material Ti6Al4V by the direct metal laser melting machine. Compression test was conducted on the scaffolds to assure meeting minimum compressive strength of human bone. The effects of pore size and topology on the cell viability of the scaffolds were analyzed. The 12 scaffolds were ultrasonically cleaned and seeded with NIH3T3 cells. Each scaffold was seeded with 1 million cells. After 32 days of culturing, the cells were fixed for their three-dimensional architecture preservation and to obtain scanning electron microscope images.

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