SafeStats: Efficient 2PC Protocols for Data Statistic-Related Functions

2026-07-28

AI Summary

This work addresses the challenge of efficiently performing privacy-preserving statistical analysis on sensitive data in domains such as healthcare and finance, where existing two-party computation (2PC) protocols suffer from poor performance for statistical functions. The authors propose SafeStats, an efficient 2PC-oriented toolkit for secure statistical analysis, which introduces three key innovations: an equality-test-free shifted frequency counting technique, a piecewise indicator-based counting sort, and a range-reduction protocol integrating binary search. These designs collectively reduce both computational and communication overhead. Experimental evaluation demonstrates that SafeStats achieves superior performance across 14 common statistical tasks; for instance, it accelerates chi-squared tests by 1.5× and reduces communication volume by 4.2× compared to generic 2PC libraries.

Abstract

Statistical analysis on sensitive datasets like medical records and financial transactions is essential for decision-making, but raises significant privacy concerns. While existing secure Two-Party Computation (2PC) makes extensive efforts in designing the common secure primitives (e.g., addition and multiplication) or machine learning-related functions, few pay attention to the statistical functions. In this paper, we propose SafeStats, a secure toolkit tailored for 2PC secure statistical analysis. Specifically, to develop SafeStats, we first refer to Microsoft Excel's statistical library and summarize that most statistical operations can be achieved with three core functions:1) frequency counting, 2) sorting, and 3) non-linear math functions. Then, for each core statistical function, SafeStats presents an efficient 2PC implementation. For secure frequency counting, SafeStats adopts a secure shift-based strategy to avoid invoking expensive 2PC equality test protocols. For secure sort, SafeStats involves a secure segment-indicator protocol to achieve secure counting-based sort, which enables fast element sorting over specific statistical scenarios without the need for secure comparison. For non-linear math functions, we enhance the current reduce-then-approximate paradigm by introducing a bisection-based range reduction protocol. Finally, we implement SafeStats and test it on 14 common statistical analysis cases. As an example, for the chi-square test, SafeStats achieves a 1.5 $\times$ runtime speedup and a 4.2 $\times$ reduction in communication compared to directly using the current general-purpose 2PC library to realize it.

Problem

• secure two-party computation
• statistical analysis
• privacy-preserving
• statistical functions
• sensitive data

Innovation

• Secure Two-Party Computation
• Statistical Functions
• Frequency Counting
• Secure Sorting
• Non-linear Math Approximation