Breaking Free: Efficient Multi-Party Private Set Union Without Non-Collusion Assumptions
Minglang Dong, Yu Chen, Cong Zhang, Yujie Bai

TL;DR
This paper introduces two novel multi-party private set union protocols: one achieving semi-honest security with oblivious transfer and symmetric-key techniques, and another attaining both linear computation and communication complexity, significantly improving efficiency.
Contribution
It presents the first semi-honest secure MPSU protocol using oblivious transfer and symmetric-key methods, and the first to achieve both linear computation and communication complexity.
Findings
The semi-honest MPSU protocol is 3.9-10.0 times faster in LAN settings.
The linear-complexity MPSU protocol reduces communication costs by up to 36.5 times.
Both protocols outperform previous work in efficiency and security assumptions.
Abstract
Multi-party private set union (MPSU) protocol enables parties, each holding a set, to collectively compute the union of their sets without revealing any additional information to other parties. There are two main categories of multi-party private set union (MPSU) protocols: The first category builds on public-key techniques, where existing works require a super-linear number of public-key operations, resulting in their poor practical efficiency. The second category builds on oblivious transfer and symmetric-key techniques. The only work in this category, proposed by Liu and Gao (ASIACRYPT 2023), features the best concrete performance among all existing protocols, but still has super-linear computation and communication. Moreover, it does not achieve the standard semi-honest security, as it inherently relies on a non-collusion assumption, which is unlikely to hold in…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCryptography and Data Security · Probability and Risk Models · Privacy-Preserving Technologies in Data
