# High-performance secure multi-party computation for data mining applications

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## Abstract

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.

## Keywords

Secure computation Performance Applications## Notes

### Acknowledgments

Authors Dan Bogdanov, Margus Niitsoo and Jan Willemson acknowledge support from the European Regional Development Fund through the Estonian Center of Excellence in Computer Science (EXCS). Authors Dan Bogdanov and Jan Willemson acknowledge support from the European Regional Development Fund through the Software Technology and Applications Competence Centre (STACC) and from the Estonian Science Foundation through grant No. 8124. Author Dan Bogdanov also acknowledges support from the European Social Fund through the Estonian Doctoral School in Information and Communication Technology (IKTDK) and the Doctoral Studies and Internationalisation Programme (DoRa). Author Tomas Toft is supported by Confidential Benchmarking, financed by The Danish Agency for Science, Technology and Innovation; and acknowledges support from the Danish National Research Foundation and The National Science Foundation of China (under the grant 61061130540) for the Sino-Danish Center for the Theory of Interactive Computation, within which part of this work was performed, as well as from the Center for Research in the Foundations of Electronic Market (supported by the Danish Strategic Research Council) within which part of this work was performed.

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