@inproceedings{a5a70e28133448e7b0c713fe16abfcda,
title = "VESTA: A Secure and Efficient FHE-based Three-Party Vectorized Evaluation System for Tree Aggregation Models",
abstract = "Machine Learning as a Service (MLaaS) platforms facilitate collaborative machine learning, but trust issues necessitate privacy-preserving methods. As for tree ensembles, prior Fully Homomorphic Encryption (FHE)-based approaches suffer from slow inference speed and high memory usage. This work proposes the VESTA system that leverages compile-time precomputation and parallelizable model partitioning to enhance performance. VESTA achieves a 2.1x speedup and reduces memory consumption by 59.4\% compared to state-of-the-art solutions.",
keywords = "domain-specific compiler, homomorphic encryption, parallelism, tree ensembles, vectorization",
author = "Haosong Zhao and Junhao Huang and Zihang Chen and Kunxiong Zhu and Donglong Chen and Zhuoran Ji and Hongyuan Liu",
note = "Publisher Copyright: {\textcopyright} 2025 Owner/Author.; 51st ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS Abstracts 2025 ; Conference date: 09-06-2025 Through 13-06-2025",
year = "2025",
month = jun,
day = "9",
doi = "10.1145/3726854.3727331",
language = "English",
series = "SIGMETRICS Abstracts 2025 - Abstracts of the 2025 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems",
pages = "31--33",
booktitle = "SIGMETRICS Abstracts 2025 - Abstracts of the 2025 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems",
}