Understanding the Tech behind SYNTHEMA

To ensure that the data remains secure during the FL process, SYNTHEMA incorporates SMPC protocols. SMPC is a cryptographic technique that allows multiple parties to compute a function collaboratively without revealing their inputs. Essentially, it enables computers to compute mathematical functions over data across multiple organizations without any one entity being able to access the complete data set. This technique ensures that the data remains secure and private throughout the entire process.

DP is another technique used by SYNTHEMA to further protect the privacy of the data. DP is a mathematical framework that guarantees that the output of any computation will not reveal any sensitive information about individual data points. In other words, even if an attacker gained access to the output of the FL process, they would not be able to identify any individual patient’s data.

By combining FL, SMPC, and DP, SYNTHEMA is creating a network that allows for the collaborative analysis of health data while protecting the privacy of the individuals and organizations contributing to the effort. This approach enables healthcare providers and researchers to gain insights from a larger pool of data without compromising privacy or security, which is essential for advancing the understanding and treatment of rare haematological diseases.

Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively train a machine learning model without directly sharing their data. In the context of SYNTHEMA, this means that participating health data centres, academic research centres, industries, and SMEs can contribute their data to a collective model without compromising the privacy of their patients or organizations.

To ensure that the data remains secure during the FL process, SYNTHEMA incorporates SMPC protocols. SMPC is a cryptographic technique that allows multiple parties to compute a function collaboratively without revealing their inputs. Essentially, it enables computers to compute mathematical functions over data across multiple organizations without any one entity being able to access the complete data set. This technique ensures that the data remains secure and private throughout the entire process.

DP is another technique used by SYNTHEMA to further protect the privacy of the data. DP is a mathematical framework that guarantees that the output of any computation will not reveal any sensitive information about individual data points. In other words, even if an attacker gained access to the output of the FL process, they would not be able to identify any individual patient’s data.

By combining FL, SMPC, and DP, SYNTHEMA is creating a network that allows for the collaborative analysis of health data while protecting the privacy of the individuals and organizations contributing to the effort. This approach enables healthcare providers and researchers to gain insights from a larger pool of data without compromising privacy or security, which is essential for advancing the understanding and treatment of rare haematological diseases.