Yujie Bai

dblp:290/3509 · DBLP profile ↗
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6ranked-venue papers
2as first author
6since 2021 · last 2026
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 3 · 3 since 2021Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
YearPublicationVenuePosition
2026 Research on non-cooperative game incentive mechanisms for task-oriented dialogue systems
Yujie Bai
Neural Comput. Appl.1
2025 Multi-Party Private Set Operations from Predicative Zero-Sharing
abstract
Typical protocols in the multi-party private set operations (MPSO) setting enable m > 2 parties to perform certain secure computation on the intersection or union of their private sets, realizing a very limited range of MPSO functionalities. Most works in this field focus on just one or two specific functionalities, resulting in a large variety of isolated schemes and a lack of a unified framework in MPSO research. In this work, we present an MPSO framework, which allows m parties, each holding a set, to securely compute any set formulas (arbitrary compositions of a finite number of binary set operations, including intersection, union and difference) on their private sets. Our framework is highly versatile and can be instantiated to accommodate a broad spectrum of MPSO functionalities. To the best of our knowledge, this is the first framework to achieve such a level of flexibility and generality in MPSO, without relying on generic secure multi-party computation (MPC) techniques. Our framework exhibits favorable theoretical and practical performance. With computation and communication complexity scaling linearly with the set size n, it achieves optimal complexity that is on par with the naive solution for widely used functionalities, such as multi-party private set intersection (MPSI), MPSI with cardinality output (MPSI-card), and MPSI with cardinality and sum (MPSI-card-sum), for the first time in the standard semi-honest model. Furthermore, the instantiations of our framework, which primarily rely on symmetric-key techniques, provide efficient protocols for MPSI, MPSI-card, MPSI-card-sum, and multi-party private set union (MPSU), with online performance that either surpasses or matches the state of the art in standard semi-honest model. At the technical core of our framework is a newly introduced primitive called predicative zero-sharing. This primitive captures the universality of a number of MPC protocols and is composable. We believe it may be of independent interest.
Minglang Dong, Yu Chen 0003, Yujie Bai, Yang Cao 0023
CCS4
2025 Efficient Multi-Party Private Set Union Without Non-Collusion Assumptions
Minglang Dong, Yujie Bai
USENIX Security Symposium3
2025 Fast Enhanced Private Set Union in the Balanced and Unbalanced Scenarios
Binbin Tu, Yujie Bai, Yang Cao 0023, Yu Chen 0003
USENIX Security Symposium2
2023 Recognizing breast tumors based on mammograms combined with pre-trained neural networks
Yujie Bai, Min Li 0093, Xiaojing Gan, Chen Chen 0078, Xiaoyi Lv
Multim. Tools Appl.1
2022 Full friendly index sets of mCn
Yurong Ji, Jinmeng Liu, Yujie Bai, Shufei Wu
Frontiers Comput. Sci.3