Efficient multi-party private set union resistant to maximum collusion attacks

Multi-party Private Set Union (MPSU) enables multiple parties to compute the union of their private sets while revealing nothing beyond the resulting union. Gao et al. (PETS 2024) proposed an efficient MPSU protocol for large-scale sets that resists maximum semi-honest collusion, where only a designated leader learns the union. Their protocol nevertheless incurs high communication overhead due to transmitting large volumes of threshold re-randomizable public-key encryption (TR-PKE) ciphertexts during interaction. In this work, we design an optimized protocol, $$\Pi _\text {MPSU}^\text {one-leader}$$, that reduces communication by using symmetric-key encryption (SKE) for most message payloads and restricting TR-PKE to a few indispensable operations. Security is preserved by separating roles. TR-PKE is used only for set items that require re-randomization and joint threshold decryption, while SKE protects all other messages carrying masked or randomized intermediate payloads. Building on $$\Pi _\text {MPSU}^\text {one-leader}$$, we further propose a leaderless protocol, $$\Pi _\text {MPSU}^\text {leaderless}$$, which allows all parties to obtain the union output and mitigates the risk of a single leader dominating in decentralized deployments. We implement our schemes and conduct a comparison with state-of-the-art. The results demonstrate that $$\Pi _\text {MPSU}^\text {one-leader}$$ achieves a 4–$$5\times$$ shrinking in communication and a 1.3–$$1.8\times$$ speedup, depending on the set size and the number of parties.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2026-03-12
DOI
https://doi.org/10.1038/s41598-026-41069-2
Primary Topic
Cryptography and Data Security
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Efficient multi-party private set union resistant to maximum collusion attacks

Joon-Woo Lee, Qiang Liu
Scientific Reports
Cryptography and Data Security
article

Efficient multi-party private set union resistant to maximum collusion attacks

Joon-Woo Lee, Qiang Liu
article en

Abstract

Multi-party Private Set Union (MPSU) enables multiple parties to compute the union of their private sets while revealing nothing beyond the resulting union. Gao et al. (PETS 2024) proposed an efficient MPSU protocol for large-scale sets that resists maximum semi-honest collusion, where only a designated leader learns the union. Their protocol nevertheless incurs high communication overhead due to transmitting large volumes of threshold re-randomizable public-key encryption (TR-PKE) ciphertexts during interaction. In this work, we design an optimized protocol, $$\Pi _\text {MPSU}^\text {one-leader}$$, that reduces communication by using symmetric-key encryption (SKE) for most message payloads and restricting TR-PKE to a few indispensable operations. Security is preserved by separating roles. TR-PKE is used only for set items that require re-randomization and joint threshold decryption, while SKE protects all other messages carrying masked or randomized intermediate payloads. Building on $$\Pi _\text {MPSU}^\text {one-leader}$$, we further propose a leaderless protocol, $$\Pi _\text {MPSU}^\text {leaderless}$$, which allows all parties to obtain the union output and mitigates the risk of a single leader dominating in decentralized deployments. We implement our schemes and conduct a comparison with state-of-the-art. The results demonstrate that $$\Pi _\text {MPSU}^\text {one-leader}$$ achieves a 4–$$5\times$$ shrinking in communication and a 1.3–$$1.8\times$$ speedup, depending on the set size and the number of parties.

Scientific Reports
Chung-Ang University (KR)
Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion
Peace, Justice and strong institutions
Openalex Percentile: Top 28%
Cryptography and Data Security
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.