Embodiments allow comparison of key figures (eg, costs, lead times) between different
entities in a privacy-preserving manner, utilizing secure multi-party computation (MPC) …

BACKGROUND [0002] Machine learning (ML) classifiers are valuable tools in many areas
such as healthcare, finance, spam filtering, intrusion detection, remote diagnosis, etc. To per …

US11190339B2 - System and method for performing equality and less than operations on
encrypted data with quasigroup operations - Google Patents US11190339B2 - System and …

Secure multi-party computation may be performed utilizing mixed protocols in order to
improve performance. In particu lar, embodiments implementing mixed protocols can reduce …

Techniques and architectures may be used to provide an environment where a data owner
storing private encrypted data in a cloud and a data evaluator may engage in a secure …

A method and system for privacy-preserving ridge regression using masks is provided. The
method includes the steps of requesting a garbled circuit from a crypto service provider …

The technology encompasses new uses of already-known cryptographic techniques. The
technology entails computer based methods of sharing information securely, in particular an …

Various embodiments are provided for performing weighted partial matching under
homomorphic encryption in a com puting environment. Selected data may be encoded and …

A method for performing privacy-preserving or secure multi-party computations enables
multiple parties to collaborate to produce a shared result while preserving the privacy of …

In a secure multi-party computation (sMPC) system, a super mask is constructed using a set
of masks corresponding to a set of data contributors. Each data contributor uses a …