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We discuss the widely increasing range of applications of a cryptographic technique called multi-party computation. For many decades, this was perceived to be of purely theoretical interest, but now it has started to find application in a number of use cases. We highlight in this paper a number of these, ranging from securing small high-value items such as cryptographic keys, through to securing an entire database. [ABSTRACT FROM AUTHOR]

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 study over a large collection of data. Ideally, such studies bring together data from many biobanks. However, data aggregation on such a large scale raises many privacy issues.Results: We show how to conduct such studies without violating privacy of individual donors and without leaking the data to third parties. The presented solution has provable security guarantees.Contact: jaak.vilo@ut.eeSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

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 framework designed with real-life efficiency in mind. It has been applied in several practical scenarios, and from these experiments, new requirements have been identified. Firstly, large datasets require more efficient protocols for standard operations such as multiplication and comparison. Secondly, the confidential processing of financial data requires the use of more complex primitives, including a secure division operation. This paper describes new protocols in the Sharemind model for secure multiplication, share conversion, equality, bit shift, bit extraction, and division. All the protocols are implemented and benchmarked, showing that the current approach provides remarkable speed improvements over the previous work. This is verified using real-world benchmarks for both operations and algorithms. [ABSTRACT FROM AUTHOR]