Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Designing an efficient and secure collaborative SQL analysis system that supports large-scale dataset inputs is a very challenging task. In this paper, we present FedQuery, an MPC-based solution for efficient and secure collaborative analysis that is able to handle billion-scale dataset inputs. FedQuery introduces novel designs in its system architecture, the underlying MPC primitives, and oblivious SQL operators as well as their combinations, significantly reducing communication and computation overhead. Comprehensive experiments on real-world datasets show that FedQuery achieves large performance improvements over state-of-the-art baselines at both the operator and query levels. Additionally, it can handle complex SQL queries on datasets up to ten billion entries in less than 14 hours.

With the rapidly-growing deployment of large language model (LLM) inference services, privacy concerns have arisen regarding to the user input data.Recent studies are exploring transforming user inputs to obfuscated embedded vectors, so that the data will not be eavesdropped by service provides.However, in this paper we show that again, without a solid and deliberate security design and analysis, such embedded vector obfuscation failed to protect users' privacy.We demonstrate the conclusion via conducting a novel inversion attack called Element-wise Differential Nearest Neighbor (EDNN) on the glide-reflection proposed in (Mishra et al., 2024), and the result showed that the original user input text can be 100% recovered from the obfuscated embedded vectors.We further analyze security requirements on embedding obfuscation and present several remedies to our proposed attack.