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This paper introduces SafeStats, a secure toolkit designed for efficient Two-Party Computation (2PC) of statistical functions, addressing a critical gap in privacy-preserving statistical analysis. By focusing on three core operations—frequency counting, sorting, and non-linear math functions—SafeStats implements innovative protocols that significantly enhance performance, achieving a 1.5× speedup and a 4.2× reduction in communication for tasks like the chi-square test. The findings highlight the potential for more efficient and secure statistical analysis on sensitive datasets, which is crucial for informed decision-making in fields like healthcare and finance.
Achieving a 4.2× reduction in communication for secure statistical analysis could revolutionize how sensitive data is processed without compromising privacy.
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.
Tsinghua AI