Dan Bogdanov

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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 on(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)
The issue of potential data misuse rises whenever it is collected from several sources. In a common setting, a large database is either horizontally or vertically partitioned between multiple entities who want to find global trends from the data. Such tasks can be solved with secure multi-party computation (MPC) techniques. However, practitioners tend to(More)
We show how to collect and analyze financial data for a consortium of ICT companies using secret sharing and secure multiparty 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)
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)
We present SecreC, a programming language for specifying privacy-preserving applications using a mix of techniques for secure multiparty computation. Building on the concept of protection domain as an abstraction of resources used to ensure the privacy of data, the SecreC language allows the specification of protection domains for different pieces of data,(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)
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)