IoT data markets in public and private institutions have become increasingly relevant in
recent years because of their potential to improve data availability and unlock new business …

Machine learning models hosted in a cloud service are increasingly popular but risk privacy:
clients sending prediction requests to the service need to disclose potentially sensitive …

Secure two-party computation is a rapidly emerging field of research and enables a large
variety of privacy-preserving applications such as mobile social networks or biometric …

In this work we present Armadillo a compilation chain used for compiling applications written
in a high-level language (C++) to work on encrypted data. The back-end of the compilation …

We address security issues in a cloud database system which employs the DBaaS model. In
such a model, a data owner (DO) exports its data to a cloud database service provider (SP) …

Decision trees and random forests are widely used classifiers in machine learning. Service
providers often host classification models in a cloud service and provide an interface for …

Secure multi-party computation is a practical cryptographic method for processing
confidential data. Research progress has led to its use in privacy-preserving statistical …

Secure Multi-Party Computation (MPC) is continuously becoming more and more practical.
Many optimizations have been introduced, making MPC protocols more suitable for solving …

We present privacy-preserving solutions for Genome-Wide Association Studies (GWAS)
based on Secure Multi-Party Computation (SMPC). Using SMPC, we protect the privacy of …

The subject matter discloses a method operated on a com puterizing system comprising
generating two secret shares of at least some of the data fields in a database, loading data …