Inpher Advises Senate Commerce Committee: Privacy-Enhancing Technologies Should Guide Data-Driven COVID-19 Response
-In advance of the Congressional hearing on ‘Enlisting Big Data in the Fight Against Coronavirus’ held April 9, 2020,…
An Alternative to Big Tech Breakup: Secure, Private Data-Sharing
-Big Tech. Regulation. Data privacy. These buzzwords have been all over the headlines in the past couple of years,…
Inpher wins International Encrypted Data Competition
-BLOOMINGTON, IN – Inpher —in collaboration with CEA and KU Leuven— won 1st place at the 2019 iDash Competition…
Inpher CEO Dr. Jordan Brandt testifies before the U.S. House Financial Services Committee on “AI and the Evolution of Cloud Computing”
-Watch Inpher CEO Dr. Jordan Brandt’s opening statement WASHINGTON, D.C. – On October 18, 2019, Inpher CEO Dr.…
Inpher Named a 2019 Gartner Cool Vendor in Privacy Preservation in Analytics
-Inpher was recognized…
Inpher Wins People’s Choice Award at FCA Financial Crime TechSprint
-The Inpher team (“Secret Computers”) implemented a secure multiparty computation protocol at the Tech Sprint…
Predictions with Privacy for Patient Data
-Recently, a group of former patients of the University of Chicago Medical Center (UCMC) had filed a class action…
Inpher presents an Innovative solution for AML/CFT at the ACPR Banque de France TechSprint
This article was originally published on AWS blogs by Conor Moran, Sr. Director, Business Development, Inpher.
AWS users occasionally need to perform analysis on data sources containing private or sensitive inputs. Inpher’s XOR Secret Computing Platform, available in AWS Marketplace, enables data scientists to train and run machine learning models while maintaining data privacy and without trading utility. Data analysis and machine learning performed by XOR can improve model performance with mathematically guaranteed data privacy while ensuring the data never leaves the data source.
Inpher’s Research on Privacy-Preserving Machine Learning Published in Two Premier Conferences
This article was originally published on AWS blogs by Conor Moran, Sr. Director, Business Development, Inpher.
AWS users occasionally need to perform analysis on data sources containing private or sensitive inputs. Inpher’s XOR Secret Computing Platform, available in AWS Marketplace, enables data scientists to train and run machine learning models while maintaining data privacy and without trading utility. Data analysis and machine learning performed by XOR can improve model performance with mathematically guaranteed data privacy while ensuring the data never leaves the data source.
How DataCo is Reinventing Data Partnerships by Putting Privacy-Enhancing Technologies to Work
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Inpher Announces Strategic Partnership with In-Q-Tel
This article was originally published on AWS blogs by Conor Moran, Sr. Director, Business Development, Inpher.
AWS users occasionally need to perform analysis on data sources containing private or sensitive inputs. Inpher’s XOR Secret Computing Platform, available in AWS Marketplace, enables data scientists to train and run machine learning models while maintaining data privacy and without trading utility. Data analysis and machine learning performed by XOR can improve model performance with mathematically guaranteed data privacy while ensuring the data never leaves the data source.
