Rmind: A Tool for Cryptographically Secure Statistical Analysis

By Dan Bogdanov, Liina Kamm, Sven Laur, Ville Sokk. 2016

71 citations recorded by the source corpus.

Secure multi-party computation is a practical cryptographic method for processing confidential data. Research progress has led to its use in privacy-preserving statistical analysis. Implementations of simple statistical analysis functions have been published, but no comprehensive tool has been built. We describe and implement a suite of most used statistical analysis functions in the privacy-preserving setting including simple statistics, t-test, chi-squared test, Wilcoxon tests and linear regression. We give descriptions of the privacy-preserving algorithms and benchmark results that show an order of magnitude improvement over previous work.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, methodology): “While CGD has been used in the context of secure computation in previous work [6], to the best of our knowledge, this modification of CGD has not been considered in the literature before.”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, background): “broad potential for practical applications, ranging from general computations of secure statistical analysis [22], [23], [25], [52], [53], [54], [55], [96], to more domainspecific uses like financial oversight [2], [21], [26], [58], biomedical computations [35], [32], [75], [84], [82], [85], [132] a”
From Keys to Databases - Real-World Applications of Secure Multi-Party Computation (2018, background): “These goals and the means to achieve them were proposed by [3] and refined in [19].”