International Association
for Cryptologic Research

Secure two-party computation (2PC)-based privacy-preserving machine learning (ML) has made remarkable progress in recent years. However, most existing works overlook the privacy challenges that arise during the data preprocessing stage. Although some recent studies have introduced efficient techniques for privacy-preserving feature selection and data alignment on well-structured datasets, they still fail to address the privacy risks involved in transforming raw data features into ML-effective numerical representations.

In this work, we present ALIOTH, an efficient 2PC framework that securely transforms raw categorical and numerical features into Weight-of-Evidence (WoE)-based numerical representations under both vertical and horizontal data partitions. By incorporating our proposed partition-aware 2PC protocols and vectorization optimizations, ALIOTH efficiently generates WoE-transformed datasets in secret. To demonstrate scalability, we conduct experiments on diverse datasets. Notably, ALIOTH can transform 3 million data samples with 100 features securely within half an hour over a wide-area network. Furthermore, ALIOTH can be seamlessly integrated with existing 2PC-based ML frameworks. Empirical evaluations on real-world financial datasets show ALIOTH improves both the predictive performance of logistic regression and 2PC training efficiency.