Abstract Most privacy-preserving machine learning methods are designed around continuous or numeric data, but categorical attributes common in many application scenarios, including clinical and health records, census survey data. Distance-based methods, particular, have limited applicability to since they do not capture the complexity of relationships among different values a attribute. Althoug...

Comparing data objects is at the heart of machine learning. For continuous data, object dissimilarity usually taken to be distance; however, for categorical there no universal agreement, categories can ordered in several different ways. Most existing category measures characterize distance among values an attribute may take using precisely number takes (the space) and frequency which they occur...

Deep learning has received much attention for computer vision and natural language processing, but less tabular data, which is the most prevalent type of data used in industry. Embeddings offer a solution by representing categorical variables as continuous vectors lowdimensional space. PyTorch provides excellent support GPU acceleration pre-built functions modules, making it easier to work with...