Learn 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)
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 show that it is possible and, indeed, feasible to use secure multiparty computation (SMC) for calculating the probability of a collision between two satellites. For this purpose, we first describe basic floating point arithmetic operators (addition and multiplication) for multiparty computations. The operators are implemented on the(More)
Secure multi-party computation platforms are becoming more and more practical. This has paved the way for privacy-preserving statistical analysis using secure multi-party computation. Simple statistical analysis functions have been emerging here and there in literature, but no comprehensive system has been compiled. We describe and implement the most used(More)
Research papers on new secure multi-party computation protocols rarely confirm the need for the developed protocol with its end users. One challenge in the way of such validation is that it is hard to explain the benefits of secure multi-party computation to non-experts. We present a method that we used to explain the application models of secure(More)
Biomedical research on human subjects often requires a large amount of data to be collected by personal interviews, Internet based questionnaires, lab measurements or by extracting data from paper or electronic health records. This data needs to be stored, analyzed and interpreted in a comprehensive manner. There is a great need for user-friendly software(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)