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Additional file 1. Figure S1. Supine blood pressure according to ��1-antitrypsin deficiency genotype. Figure S2. Ankle-brachial index according to ��1-antitrypsin deficiency genotype. Figure S3. Risk of cardiovascular disease according to ��1-antitrypsin deficiency genotype by step-up logistic regression. Figure S4. Risk of cardiovascular disease according to ��1-antitrypsin deficiency genotype, stratified by sex, age, smoking status and COPD. Figure S5. Binscatter plots of systolic blood pressure, ischemic heart disease, ischemic cerebrovascular disease, or hypertension versus plasma ��1-antitrypsin partitioned into 20 bins. Figure S6. Exclusion sensitivity plot. Figure S7. Systolic blood pressure according to ��1-antitrypsin deficiency genotype, stratified by study population.

Additional file 1.

We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abstraction allows one to implement complex privacy preserving constructs such as Federated Learning, Secure Multiparty Computation, and Differential Privacy while still exposing a familiar deep learning API to the end-user. We report early results on the Boston Housing and Pima Indian Diabetes datasets. While the privacy features apart from Differential Privacy do not impact the prediction accuracy, the current implementation of the framework introduces a significant overhead in performance, which will be addressed at a later stage of the development. We believe this work is an important milestone introducing the first reliable, general framework for privacy preserving deep learning., Comment: PPML 2018, 5 pages