TY - JOUR AU - Carpov, Sergiu AU - Gama, Nicolas AU - Georgieva, Mariya AU - Troncoso-Pastoriza, Juan Ramon PY - 2020 DA - 2020/07/21 TI - Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption JO - BMC Medical Genomics SP - 88 VL - 13 IS - 7 AB - Privacy-preserving computations on genomic data, and more generally on medical data, is a critical path technology for innovative, life-saving research to positively and equally impact the global population. It enables medical research algorithms to be securely deployed in the cloud because operations on encrypted genomic databases are conducted without revealing any individual genomes. Methods for secure computation have shown significant performance improvements over the last several years. However, it is still challenging to apply them on large biomedical datasets. SN - 1755-8794 UR - https://doi.org/10.1186/s12920-020-0723-0 DO - 10.1186/s12920-020-0723-0 ID - Carpov2020 ER -