JOURNAL ARTICLE

Quantum multi-party private set union protocol based on least common multiple and Shor's algorithm.

  • Published In: International Journal of Quantum Information, 2023, v. 21, n. 7. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Liu, Wenjie; Yang, Qi; Li, Zixian 3 of 3

Abstract

Private set union (PSU) allows several parties to obtain the union of their private sets without disclosing each party's private information. Existing PSU protocols often have polynomial complexity for the complete set size or complicated process. In this paper, a quantum multi-party PSU protocol based on least common multiple (LCM) and Shor's algorithm is proposed, which enables the union of multiple sets to be computed all at once. In order to increase the one-time success probability of the protocol, we first improved Shor's period-finding algorithm, which is used in LCM computation and integer factoring. Each party's private set is encoded into an integer obtained by multiplying several prime numbers, thus the PSU problem is transformed into an LCM problem. The LCM of these integers is computed by using the improved Shor's period-finding algorithm, and then factored to derived the union set. We prove the correctness of the proposed protocol, and its unconditional security against semi-honest attacks. Complexity analysis shows that our protocol has logarithmic complexity for the complete set size. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Quantum Information. 2023/10, Vol. 21, Issue 7, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2023
  • ISSN:0219-7499
  • DOI:10.1142/S0219749923400063
  • Accession Number:172369402
  • Copyright Statement:Copyright of International Journal of Quantum Information is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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