Haris Smajlović, University of Victoria; David Froelicher, MIT; Ariya Shajii, Exaloop Inc.; Bonnie Berger, MIT; Hyunghoon Cho, Yale University; Ibrahim Numanagić, University of Victoria
We present Shechi, an easy-to-use programming framework for secure high-performance computing on distributed datasets. Shechi automatically converts Pythonic code into a secure distributed equivalent using multiparty homomorphic encryption (MHE), combining homomorphic encryption (HE) and secure multiparty computation (SMC) techniques to enable efficient distributed computation. Shechi abstracts away considerations about the private and distributed aspects of the input data from end users through a familiar Pythonic syntax. Our framework introduces new data types for the efficient handling of distributed data as well as systematic compiler optimizations for cryptographic and distributed computations. We evaluate Shechi on a wide range of applications, including principal component analysis and complex genomic analysis tasks. Our results demonstrate Shechi's ability to uncover optimizations missed even by expert developers, achieving up to 15× runtime improvements over the prior state-of-the-art solutions and a 40-fold improvement in overall code expressiveness compared to manually optimized code. Shechi represents the first MHE compiler, extending secure computation frameworks to the analysis of sensitive distributed datasets.
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