Optimizing Secure Statistical Computations with PICCO

TL;DR

This paper evaluates and optimizes the performance of secure statistical computations, specifically chi-squared and standard deviation, using the PICCO compiler to ensure privacy-preserving data analysis.

Contribution

It provides an assessment and optimization of statistical programs on PICCO, demonstrating improved performance for secure multi-party computations.

Findings

Optimized implementations of chi-squared and standard deviation in PICCO.

Secure computations achieved comparable performance to non-secure counterparts.

Enhanced understanding of performance trade-offs in privacy-preserving statistical analysis.

Abstract

Growth in research collaboration has caused an increased need for sharing of data. However, when this data is private, there is also an increased need for maintaining security and privacy. Secure multi-party computation enables any function to be securely evaluated over private data without revealing any unintended data. A number of tools and compilers have been recently developed to support evaluation of various functionalities over private data. PICCO is one of such compilers that transforms a general-purpose user program into its secure distributed implementation. Here we assess performance of common statistical programs using PICCO. Specifically, we focus on chi-squared and standard deviation computations and optimize user programs for them to assess performance that an informed user might expect from securely evaluating these functions using a general-purpose compiler.