Siemens Healthineers Ag

Siemens Healthcare Gmbh

Biosense Webster, Inc.

Srikrishna Prasad

Michael Kelm

Matityahu Amit

Ute Rosenbaum

Anthony Jay

Shylashree Nagaraja

Srividya Tirunellai Rajamani

Manuel Sujith

Amatzia Tov

Daniel Nottebrock

Respective local parameters parametrizing a base function are determined to provide at least one local trained function for each of multiple client systems by training the respective local trained function using machine learning with multiple training datasets on the respective client system, wherein at least some of the training datasets are specific to the respective client system. A respective local plaintext dataset including the local parameters of the respective local trained function is encrypted to generate a respective local encrypted dataset on the respective client system. The local encrypted datasets are transmitted to an aggregating system, and the global encrypted dataset is calculated from the local encrypted datasets using the calculation algorithm.

Computer-implemented method for providing an encrypted dataset providing a global trained function, computer-implemented method for recovering personal information, computer system and computer program

A gateway and a method are provided for securely storing (and/or securely retrieving) medical data the method for storing comprising at least steps of:

Method for securely storing and retrieving medical data

Various examples of embodiments of the invention generally relate to automating a clinical workflow, the clinical workflow including an analysis of one or more medical datasets and generation of a medical report based on the analysis. For example, machine-learning algorithms may be used for the analysis. The medical report may be generated based on one or more report templates.

Automated clinical workflow

A computer-implemented method is for providing processed data. In an embodiment, the method includes receiving, by a first encryption entity, first plaintext data including a matrix of numbers; determining, by the first encryption entity, an encryption key including an integer matrix; homomorphically encrypting, by the first encryption entity, the first plaintext data based on a matrix multiplication of the first plaintext data and the encryption key, to generate first encrypted data; sending, by the first encryption entity, the first encrypted data to a processing entity; receiving, by a decryption entity, encrypted processed data from the processing entity, the encrypted processed data being based on the first encrypted data; decrypting, by the decryption entity, the encrypted processed data based on a matrix multiplication of the processed data and an inverse of the encryption key, to generate processed data; and providing, by the decryption entity, the processed data.

Anonymized generating and proving of processed data

Growing community of inventors

Erlangen, Germany

Srikrishna Prasad