International Journal of Information Security

, Volume 11, Issue 6, pp 403–418 | Cite as

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

  • Dan Bogdanov
  • Margus Niitsoo
  • Tomas Toft
  • Jan Willemson
Regular Contribution

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 

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Dan Bogdanov
    • 1
    • 2
  • Margus Niitsoo
    • 2
  • Tomas Toft
    • 3
  • Jan Willemson
    • 1
    • 4
  1. 1.CyberneticaTartuEstonia
  2. 2.Institute of Computer ScienceUniversity of TartuTartuEstonia
  3. 3.Department of Computer ScienceAarhus UniversityAarhus NDenmark
  4. 4.STACCTartuEstonia

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