Inpher presents an Innovative solution for AML/CFT at the ACPR…
Inpher participated in the ACPR Banque de France TechSprint in Paris on September 13 and demonstrated a commercial solution using the XOR Platform for banks to securely...
Inpher’s Research on Privacy-Preserving Machine Learning Published in Two Premier…
As our reliance on data grows, the need for data privacy becomes even more critical to everyone – and for businesses, an imperative. For Inpher, our focus has been on two...
How DataCo is Reinventing Data Partnerships by Putting Privacy-Enhancing Technologies…
Think of all the great customer experiences you have ever had! Those unexpected business class upgrades or Netflix “on-the-house” as a part of your cell phone plans....
Inpher Announces Strategic Partnership with In-Q-Tel
We are thrilled to announce today a strategic partnership with In-Q-Tel (IQT), a not-for-profit organization that identifies and accelerates the development and delivery of...
Flying Fuzzy: A Privacy Preserved No-Fly List for Global Airlines…
Since 2020, airlines have been dealing with a rash of irate, disruptive, and violent passengers, resulting in many of those travelers being banned from their future flights....
Neuroimaging Based Diagnosis of Alzheimer’s Disease Using Privacy-Preserving Machine Learning…
This second of the three-part blog series describes two privacy-preserving machine learning models (linear and logistic regression) for detecting Alzheimer's disease. The...
Neuroimaging Based Diagnosis of Alzheimer’s Disease Using Privacy-Preserving Machine Learning…
In this first of a three-part blog series, we present a viable solution designed by clinical researchers at CHUV and Inpher aiming to build privacy-preserving...
Building Privacy-Preserving Decision Tree Models Using Multi-Party Computation
Machine learning (ML) is increasingly important in a wide range of applications, including market forecasting, service personalization, voice and facial recognition,...
Privacy Challenges in Extreme Gradient Boosting
Machine learning (ML) is increasingly important in a wide range of applications, including market forecasting, service personalization, voice and facial recognition,...
How to Build Machine Learning Models with Private Data Sources…
AWS users occasionally need to perform analysis on data sources containing private or sensitive inputs. Inpher’s XOR Secret Computing Platform, available in AWS...
Inpher Wins Tech4Trust 2021 Startup Accelerator Program
Organizations today face unprecedented challenges in leveraging the data they own: access to sensitive data needs to be controlled more efficiently; authentication methods...
EU and U.S. Policymakers Emphasize Privacy-Enhancing Technologies as a Shared…
Many consumers use phones and IoT devices that rely on FL every day without knowing what it is. This means that the novelty of FL is shielding scrutiny on its inherent—and...
The Privacy Risk Right Under Our Nose in Federated Learning
Many consumers use phones and IoT devices that rely on FL every day without knowing what it is. This means that the novelty of FL is shielding scrutiny on its inherent—and...
Inpher wins the iDASH Secure Genome Analysis Competition
Advances in biomedical analytics and AI have revolutionized modern healthcare. Predictive systems in this field allow for better medical and epidemiological research, as well...
Accelerating Moore4Medical’s Innovation: Ensuring Patient Privacy in Healthcare AI
Moore4Medical is a project pioneered by Philips, leading healthcare providers and hospitals in Europe. Inpher is one of the four Swiss companies selected by this consortium,...