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EMPSI: Efficient multiparty private set intersection (with cardinality)

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Abstract

Multiparty private set intersection (PSI) allows several parties, each holding a set of elements, to jointly compute the intersection without leaking any additional information. With the development of cloud computing, PSI has a wide range of applications in privacy protection. However, it is complex to build an efficient and reliable scheme to protect user privacy.

To address this issue, we propose EMPSI, an efficient PSI (with cardinality) protocol in a multiparty setting. EMPSI avoids using heavy cryptographic primitives (mainly rely on symmetric-key encryption) to achieve better performance. In addition, both PSI and PSI with the cardinality of EMPSI are secure against semi-honest adversaries and allow any number of colluding clients (at least one honest client). We also do experiments to compare EMPSI with some state-of-the-art works. The experimental results show that proposed EMPSI (-CA) has better performance and is scalable in the number of clients and the set size.

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China (2020YFA0712300), in part by the National Natural Science Foundation of China (Grant Nos. 62172162, 62132005), and in part by the Shanghai Trusted Industry Internet Software Collaborative Innovation Center.

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Authors and Affiliations

  1. Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, 200062, China

    Yunbo Yang, Xiaolei Dong, Zhenfu Cao, Jiachen Shen, Ruofan Li & Yihao Yang

  2. Kunyao Academy of Shanghai Kunyao Network Technology Co., Ltd., Shanghai, 201203, China

    Yunbo Yang

  3. PwC US Advisory Shanghai AC, Shanghai, 200233, China

    Shangmin Dou

Authors
  1. Yunbo Yang
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  2. Xiaolei Dong
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  3. Zhenfu Cao
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  4. Jiachen Shen
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  5. Ruofan Li
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  6. Yihao Yang
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  7. Shangmin Dou
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Corresponding authors

Correspondence to Xiaolei Dong, Zhenfu Cao or Jiachen Shen.

Additional information

Yunbo Yang is a doctoral student in East China Normal University, China. His tutor is Xiaolei Dong and his main research direction is secure multiparty computation (MPC), searchable encryption (SE), and zero-knowledge proof (ZK). He also works as a cryptographic researcher in Shanghai Kunyao Network Technology Co., Ltd. and is responsible for the design of cryptographic algorithm.

Xiaolei Dong received her PhD degree at Harbin Institute of Technology, China in 2001. She is a doctoral supervisor in East China Normal University, China. Her research interests include number theory, cryptography and network security (cloud computing, cloud processing security and privacy protection, big data security and privacy protection, etc.).

Zhenfu Cao is a doctoral supervisor in East China Normal University, China. His research interests include number theory, cryptography and new theories of network security (cloud computing, cloud processing security and privacy protection, big data security and privacy protection, etc.).

Jiachen Shen received his Bachelor degree at Shanghai Jiao Tong University, China in 2001, his Master and PhD degrees at University of Louisiana at Lafayette, USA in 2003 and 2008, respectively. He joined East China Normal University, China in 2015. His research interests include applied cryptography, cloud security, searchable encryption, and blockchains.

Ruofan Li is a postgraduate student in East China Normal University, China. His tutor is Zhenfu Cao and he is co-author with Yunbo Yang. His research interest is multiparty computation (MPC), private set intersection (PSI), and searchable encryption (SE).

Yihao Yang is a postgraduate student in East China Normal University, China. He received his Bachelor degree in Shanghai Ocean University, China in 2020. His tutor is Xiaolei Dong. His research interest is multiparty computation (MPC), private set intersection (PSI), and searchable encryption (SE).

Shangmin Dou received his Bachelor degree of computer science in Shanghai Ocean University, China in 2020. He has a very solid development skill. He is now working as a tech lead in PwC.

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Yang, Y., Dong, X., Cao, Z. et al. EMPSI: Efficient multiparty private set intersection (with cardinality). Front. Comput. Sci. 18, 181804 (2024). https://doi.org/10.1007/s11704-022-2269-0

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