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Shechi: A Secure Distributed Computation Compiler Based on Multiparty Homomorphic Encryption.

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Shechi is a new programming framework that enables secure high-performance computing on distributed datasets. It uses multiparty homomorphic encryption (MHE) to efficiently process sensitive data while maintaining privacy.

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Area of Science:

  • Computer Science
  • Cryptography
  • Distributed Systems

Background:

  • Securely computing on distributed datasets is challenging.
  • Existing methods often require complex manual optimization or compromise privacy.
  • There is a need for user-friendly frameworks that combine security and performance.

Purpose of the Study:

  • To introduce Shechi, a novel programming framework for secure high-performance computing.
  • To enable efficient distributed computation on sensitive datasets using multiparty homomorphic encryption (MHE).
  • To abstract data privacy and distribution complexities from end-users.

Main Methods:

  • Shechi automatically converts Pythonic code into a secure distributed equivalent.
  • It combines homomorphic encryption (HE) and secure multiparty computation (SMC) techniques.
  • The framework introduces new data types and compiler optimizations for cryptographic and distributed tasks.

Main Results:

  • Shechi achieves up to 15x runtime improvements over prior solutions.
  • It offers a 40-fold improvement in code expressiveness compared to expert-optimized code.
  • Evaluated on applications like principal component analysis and genomic analysis.

Conclusions:

  • Shechi is the first MHE compiler, extending secure computation to sensitive distributed data analysis.
  • The framework simplifies secure distributed computing, offering significant performance and expressiveness gains.
  • It empowers users to analyze sensitive data without compromising privacy or requiring deep cryptographic expertise.