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Gathering and processing sensitive data is a difficult task. In fact, there is no common recipe for building the necessary information systems. In this paper , we present a provably secure and efficient general-purpose computation system to address this problem. Our solution—SHAREMIND—is a virtual machine for privacy-preserving data processing that relies(More)
Secure multi-party computation (MPC) is a technique well suited for privacy-preserving data mining. Even with the recent progress in two-party computation techniques such as fully homomorphic encryption, general MPC remains relevant as it has shown promising performance metrics in real-world benchmarks. Sharemind is a secure multi-party computation(More)
MOTIVATION Increased availability of various genotyping techniques has initiated a race for finding genetic markers that can be used in diagnostics and personalized medicine. Although many genetic risk factors are known, key causes of common diseases with complex heritage patterns are still unknown. Identification of such complex traits requires a targeted(More)
In this paper we describe a secure system for jointly collecting and analyzing financial data for a consortium of ICT companies. To guarantee each participant's privacy, we use secret sharing and secure multi-party computation (MPC) techniques. While MPC has been used to solve real-life problems beforehand, this is the first time where the actual MPC(More)
The Estonian Tax and Customs Board (MTA) has identified that Estonia is losing over 220 million euros a year due to avoidance of value-added tax (VAT). The parliament proposed legislation that makes companies declare their purchase and sales invoices for automated risk analysis and fraud detection. The law was vetoed by the Estonian President on the grounds(More)
The popularity of virtual worlds and their increasing economic impact has created a situation where the value of trusted identification has risen substantially. We propose an identity management solution that provides the user with secure credentials and allows to decrease the required trust that the user must have towards the server running the virtual(More)
We describe the use of secure multi-party computation for performing a large-scale privacy-preserving statistical study on real government data. In 2015, statisticians in Estonia conducted a big data study to look for correlations between working during university studies and failing to graduate in time. The study was conducted by linking the database of(More)
We show how to collect and analyze financial data for a consortium of ICT companies using secret sharing and secure multi-party computation (MPC). This is the first time where the actual MPC computation on real data was done over the internet with computing nodes spread geographically apart. We describe the technical solution and present user feedback(More)
Secure Multi-party Computation (SMC) is seen as one of the main enablers for secure outsourcing of computation. Currently, there are many different SMC techniques (garbled circuits, secret sharing, homomorphic encryption, etc.) and none of them is clearly superior to others in terms of efficiency, security guarantees, ease of implementation, etc. For(More)
The quality of empirical statistical studies is tightly related to the quality and amount of source data available. However, it is often hard to collect data from several sources due to privacy requirements or a lack of trust. In this paper, we propose a novel way to combine secure multi-party computation technology with federated database systems to(More)